{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c979f719-a7bd-4392-86f9-f699220b2c93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP20</th>\n",
       "      <th>COUNTYFP20</th>\n",
       "      <th>TRACTCE20</th>\n",
       "      <th>GEOID20</th>\n",
       "      <th>NAME20</th>\n",
       "      <th>NAMELSAD20</th>\n",
       "      <th>MTFCC20</th>\n",
       "      <th>FUNCSTAT20</th>\n",
       "      <th>ALAND20</th>\n",
       "      <th>AWATER20</th>\n",
       "      <th>...</th>\n",
       "      <th>P0050002</th>\n",
       "      <th>P0050003</th>\n",
       "      <th>P0050004</th>\n",
       "      <th>P0050005</th>\n",
       "      <th>P0050006</th>\n",
       "      <th>P0050007</th>\n",
       "      <th>P0050008</th>\n",
       "      <th>P0050009</th>\n",
       "      <th>P0050010</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>26</td>\n",
       "      <td>163</td>\n",
       "      <td>591900</td>\n",
       "      <td>26163591900</td>\n",
       "      <td>5919</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>5033701</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>POLYGON ((-83.24385 42.08143, -83.24375 42.081...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26</td>\n",
       "      <td>163</td>\n",
       "      <td>592000</td>\n",
       "      <td>26163592000</td>\n",
       "      <td>5920</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>12758140</td>\n",
       "      <td>4008857</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>POLYGON ((-83.23187 42.07831, -83.23075 42.079...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>26</td>\n",
       "      <td>163</td>\n",
       "      <td>593200</td>\n",
       "      <td>26163593200</td>\n",
       "      <td>5932</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2056711</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>POLYGON ((-83.25654 42.15356, -83.25327 42.153...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>163</td>\n",
       "      <td>594000</td>\n",
       "      <td>26163594000</td>\n",
       "      <td>5940</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>5032161</td>\n",
       "      <td>169441</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>POLYGON ((-83.18951 42.14131, -83.18868 42.142...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>163</td>\n",
       "      <td>594100</td>\n",
       "      <td>26163594100</td>\n",
       "      <td>5941</td>\n",
       "      <td>Census Tract</td>\n",
       "      <td>G5020</td>\n",
       "      <td>S</td>\n",
       "      <td>2479041</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>POLYGON ((-83.20697 42.14824, -83.20675 42.150...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 345 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP20 COUNTYFP20 TRACTCE20      GEOID20 NAME20    NAMELSAD20 MTFCC20  \\\n",
       "0        26        163    591900  26163591900   5919  Census Tract   G5020   \n",
       "1        26        163    592000  26163592000   5920  Census Tract   G5020   \n",
       "2        26        163    593200  26163593200   5932  Census Tract   G5020   \n",
       "3        26        163    594000  26163594000   5940  Census Tract   G5020   \n",
       "4        26        163    594100  26163594100   5941  Census Tract   G5020   \n",
       "\n",
       "  FUNCSTAT20   ALAND20  AWATER20  ... P0050002 P0050003 P0050004 P0050005  \\\n",
       "0          S   5033701         0  ...        0        0        0        0   \n",
       "1          S  12758140   4008857  ...        0        0        0        0   \n",
       "2          S   2056711         0  ...        0        0        0        0   \n",
       "3          S   5032161    169441  ...        0        0        0        0   \n",
       "4          S   2479041         0  ...        0        0        0        0   \n",
       "\n",
       "  P0050006 P0050007 P0050008 P0050009 P0050010  \\\n",
       "0        0        7        0        0        7   \n",
       "1        0        8        0        0        8   \n",
       "2        0        2        0        0        2   \n",
       "3        0        0        0        0        0   \n",
       "4        0       12        0        0       12   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-83.24385 42.08143, -83.24375 42.081...  \n",
       "1  POLYGON ((-83.23187 42.07831, -83.23075 42.079...  \n",
       "2  POLYGON ((-83.25654 42.15356, -83.25327 42.153...  \n",
       "3  POLYGON ((-83.18951 42.14131, -83.18868 42.142...  \n",
       "4  POLYGON ((-83.20697 42.14824, -83.20675 42.150...  \n",
       "\n",
       "[5 rows x 345 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this is for working with census tracts and VTD precincts\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import shapely\n",
    "import geopandas as gpd\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import random\n",
    "from scipy.stats import norm\n",
    "import math\n",
    "tractPopFile = gpd.read_file(\"state_map_files/mi_pl2020_t.dbf\") #for Texas, need only this file\n",
    "tractPopFile.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e4af1be-0f6a-4c46-9d23-da70d63c30e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#handy function for plotting Polygon or multiPolygon tracts and precincts\n",
    "def plotPoly(inputPoly):\n",
    "    simplePoly = Polygon([(0,0),(0,1),(1,1)])\n",
    "    if inputPoly.type == simplePoly.type:\n",
    "        x,y = inputPoly.exterior.xy\n",
    "        plt.plot(x,y)\n",
    "    else:\n",
    "        for geom in inputPoly.geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a7a90b04-4b48-4ab8-9195-8d953907c3fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "there are 3017 popn tracts for MI\n"
     ]
    }
   ],
   "source": [
    "# EXTRACT TRACT GEOMETRIES AND POPULATIONS INTO LISTS, COMPUTE TRACT AREAS\n",
    "# If the population and geometry data are in one file, this should work.\n",
    "STATE = \"MI\"\n",
    "tractGeom = tractPopFile['geometry']  #for some states, replace with tractGeomFile\n",
    "tractPop = tractPopFile['P0010001']\n",
    "tractVAP = tractPopFile['P0030001']   #NEW 3/2/22 - USE VAP\n",
    "# tractPop2 = tractPopFile['P0020001']   #not needed; confirmed that this matches P00100001 exactly\n",
    "tractHisp = tractPopFile['P0040002']   #NEW 3/2/22 - USE VAP\n",
    "tractBlack = tractPopFile['P0030004']  #NEW 3/2/22 - USE VAP\n",
    "nTracts = len(tractPop)\n",
    "print(\"there are {0} popn tracts for {1}\".format(nTracts, STATE) )\n",
    "tractArea = [0.]*nTracts\n",
    "for t in range (0,nTracts) :\n",
    "    tractArea[t] = tractGeom[t].area\n",
    "isSkippedTract = [0] *nTracts  #this will house a temporary list of tracts for manipulation\n",
    "tractPop = tractPop.to_numpy()  #to avoid panda overwrite grousing\n",
    "tractBlack= tractBlack.to_numpy()\n",
    "tractHisp = tractHisp.to_numpy()\n",
    "tractVAP = tractVAP.to_numpy()\n",
    "stateVAP = np.sum(tractVAP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c90b3908-e2e3-46b8-98e5-ae285eca1ee3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is Hispanic population by total population MI voter-age population\n",
      "state pop= 10077331 VAP pct Hispanic is  0.04645148296781064\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Hispanic pop to total pop?\n",
    "print(\"this is Hispanic population by total population \"+STATE,\"voter-age population\")\n",
    "print(\"state pop=\",np.sum(tractPop), \"VAP pct Hispanic is \",np.sum(tractHisp)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract Voter Age Population\", ylabel=\"tract Hispanic pop\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractHisp, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "237df16d-af87-47ba-acb1-2ef5702c1406",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is voter-age Black population by total VAP MI\n",
      "total state pop= 10077331 , VAP pct Black is  0.1302837464221195\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our correlation of Black pop to total pop?\n",
    "print(\"this is voter-age Black population by total VAP \"+STATE)\n",
    "print(\"total state pop=\",np.sum(tractPop), \", VAP pct Black is \",np.sum(tractBlack)/np.sum(tractVAP) )\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"tract VAP\", ylabel=\"tract Black VAP\")\n",
    "x = [0,10000]\n",
    "y = [0,10000]\n",
    "plt.plot(tractVAP, tractBlack, marker='.',linestyle=\"none\")\n",
    "plt.plot(x,y,linestyle = 'dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "10b208af-8eb0-45fa-9dc0-8873eb797200",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of Census tract population for MI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for curiosity, let's plot the distribution of populations among tracts\n",
    "n_bins=50\n",
    "print(\"this is a histogram of Census tract population for \"+STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractPop, bins=n_bins)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4aac60d6-4cce-469b-b075-ad743acda38c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is population by Census tract for MI\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of tract populations by area?\n",
    "print(\"this is population by Census tract for \"+STATE)  \n",
    "plt.plot(tractPop, tractArea, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f159bf55-a25e-45c0-b7ea-4c21fbce9bb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'shapely.geometry.polygon.Polygon'> <class 'shapely.geometry.polygon.Polygon'>\n"
     ]
    }
   ],
   "source": [
    "print( type(tractGeom[0]),type(tractGeom[1]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e8f8f92-a336-43e5-8ac5-20c657cb5857",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for nonPolygons in census tract data\n",
      "2215 2721 0.04803668652100095 nonPolygon tract no, pop, area\n",
      "4.374797700001456e-05\n",
      "0.04799293854400094\n",
      "2335 2334 0.016255610913499863 nonPolygon tract no, pop, area\n",
      "0.014644414102499809\n",
      "0.0016111968110000528\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"looking for nonPolygons in census tract data\")\n",
    "notPoly = [0]*nTracts\n",
    "for m in range(nTracts):\n",
    "    if type(tractGeom[m]) != type(tractGeom[1]):  #assuming tract 1 is a single polygon\n",
    "        notPoly[m] = 1\n",
    "        print(m,tractPop[m], tractArea[m], \"nonPolygon tract no, pop, area\")\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 :  #let's zoom in on these\n",
    "            for geom in tractGeom[m].geoms :\n",
    "                xg,yg = geom.exterior.xy\n",
    "                print(geom.area)\n",
    "                plt.plot(xg,yg)\n",
    "            plt.text(x+0.0,y+0.01,m,fontsize=9)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fb68f44b-eb19-4993-b856-cfa9c855b2e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#little code to remove nonperforming tiny subtract from a tract.  Not used for most states\n",
    "counter = 0\n",
    "for geom in tractGeom[605].geoms:\n",
    "    if counter ==1 :\n",
    "        fixedGeom = geom\n",
    "    counter +=1\n",
    "tractGeom[605] = fixedGeom\n",
    "plotPoly(tractGeom[605])\n",
    "notPoly[606] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d34b5fe5-6f02-413a-8c7b-c6662fa756ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tzGw+4ZH+Fe7uZtbSzKYAuPts4AXgE+DzyOuNqmbNIiJSBebu8a5hN9nZ2Z6TkxPvMkRE9hlmNsfds6Pt0ydjRUQCTkEvIhJwCnoRkYBT0IuIBJyCXkQk4BT0IiIBp6AXEQk4Bb2ISMAp6EVEAk5BLyIScAp6EZGAU9CLiAScgl5EJOAU9CIiAaegFxEJOAW9iEjAKehFRAIu5qA3s5CZfWpmk0ttu8nMFpnZAjO7v5x+jczsBTP70swWmtkJNVG4iIjEJrEKbYcQvrB3GoCZ9QT6AlnuXmBmzcrpNxKY6u6/NLNkILU6BYuISNXENKI3swzgHGB0qc3XAcPdvQDA3ddE6ZcGnAw8GWlT6O4bqlmziIhUQaxTNyOA24GSUtvaAz3MbLaZvWNmXaP0awfkAWMi0z6jzeyAaC9gZoPNLMfMcvLy8qpwCCIiUpFKg97M+gBr3H1OmV2JQGOgG3AbMNHMLEqbY4DH3f1oYAtwZ7TXcfdR7p7t7tnp6elVPAwRESlPLCP67sB5ZrYEGA/0MrOxQC4wycM+Ijzab1qmby6Q6+6zI49fIBz8IiJSSyoNene/y90z3D0T6AfMcPf+wMtALwAzaw8kA2vL9F0NLDezwyObTgO+qLHqRUSkUtVZR/8U0M7M5hMe6V/h7m5mLc1sSql2NwHjzOwzoAtwXzVeU0REqsjcPd417CY7O9tzcnLiXYaIyD7DzOa4e3a0ffpkrIhIwCnoRUQCTkEvIhJwCnoRkYBT0IuIBJyCXkQk4BT0IiIBp6AXEQk4Bb2ISMAp6EVEAk5BLyIScAp6EZGAU9CLiAScgl5EJOAU9CIiAaegFxEJuJiD3sxCZvapmU0ute0mM1tkZgvM7P6q9BURkdqRWIW2Q4CFQBqAmfUE+gJZ7l5gZs1i7SsiIrUnphG9mWUA5wCjS22+Dhju7gUA7r6mCn1FRKSWxDp1MwK4HSgpta090MPMZpvZO2bWtQp9d2Nmg80sx8xy8vLyYixLREQqU2nQm1kfYI27zymzKxFoDHQDbgMmmpnF2Hc37j7K3bPdPTs9PT3mAxARkYrFMkffHTjPzHoDKUCamY0FcoFJ7u7AR2ZWAjQF8irr6+79a/QoRESkXJWO6N39LnfPcPdMoB8wIxLULwO9AMysPZAMrI2xr4iI1JLqrKN/CmhnZvOB8cAV7u5m1tLMptRMeSIiUl0WnnmpW7Kzsz0nJyfeZYiI7DPMbI67Z0fbp0/GiogEnIJeRCTgFPQiIgGnoBcRCTgFvYhIwCnoRUQCTkEvIhJwCnoRkYBT0IuIBJyCXkQk4BT0IiIBp6AXEQk4Bb2ISMAp6EVEAk5BLyIScAp6EZGAiznozSxkZp+a2eRS224ys0VmtsDM7o/Sp7WZvWVmCyNthtRU4SIiEptYLg6+0xBgIZAGYGY9gb5AlrsXmFmzKH2KgFvd/RMzawjMMbPX3f2L6hYuIiKxiWlEb2YZwDnA6FKbrwOGu3sBgLuvKdvP3Ve5+yeR+5sI/6FoVd2iRUQkdrFO3YwAbgdKSm1rD/Qws9lm9o6Zda3oCcwsEzgamF3O/sFmlmNmOXl5eTGWJSIilak06M2sD7DG3eeU2ZUINAa6AbcBE83MynmOBsCLwC3unh+tjbuPcvdsd89OT0+vyjGIiEgFYpmj7w6cZ2a9gRQgzczGArnAJHd34CMzKwGaArsMx80siXDIj3P3STVavYiIVKrSEb273+XuGe6eCfQDZrh7f+BloBeAmbUHkoG1pftGRvhPAgvd/cGaLV1ERGJRnXX0TwHtzGw+MB64wt3dzFqa2ZRIm+7AAKCXmc2N3HpXs2YREamCqiyvxN3fBt6O3C8E+kdpsxLoHbk/E4g6by8iIrVDn4wVEQk4Bb2ISMAp6EVEAk5BLyIScAp6EZGAU9CLiAScgl5EJOAU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8iEnAKehGRgFPQi4gEnIJeRCTgFPQiIgEXc9CbWcjMPjWzyaW23WRmi8xsgZndX06/syJtFpvZnTVRtIiIxK4qV5gaAiwE0gDMrCfQF8hy9wIza1a2g5mFgEeBMwhfTPxjM3vF3b+oduUiIhKTmEb0ZpYBnAOMLrX5OmC4uxcAuPuaKF2PAxa7+7eRSw+OJ/zHQUREakmsUzcjgNuBklLb2gM9zGy2mb1jZl2j9GsFLC/1ODeybTdmNtjMcswsJy8vL8ayRESkMpUGvZn1Ada4+5wyuxKBxkA34DZgopmVvRB4tAuDe7TXcfdR7p7t7tnp6emVVy4iIjGJZY6+O3CemfUGUoA0MxtLeHQ+yd0d+MjMSoCmQOnheC7QutTjDGBljVQuIiIxqXRE7+53uXuGu2cC/YAZ7t4feBnoBWBm7YFkYG2Z7h8Dh5lZWzNLjvR/pebKFxGRylRnHf1TQDszm0/4TdYr3N3NrKWZTQFw9yLgRmAa4RU7E919QXWLFhGR2Fl45qVuyc7O9pycnHiXISKyzzCzOe6eHW2fPhkrIhJwCnoRkYBT0IuIBJyCXkQkipKSEiY//C++mj033qVUW1W+60ZEZL/x6dSZLJo1kUWzJtLhpFM5+fJBNDioSbzL2iMa0YuIRDHvzQ8BCNU7hkUfzOSpW65h9ksTKSosjHNlVaegFxGJYvPaJVioOUmpp5J+yLW0yerCzPH/4ulh1/P1xx9QF5eml0dBLyJShruzY/tqEhJbANDqiEz6Dvsdv/ztvSQmJfPKA3/mxfv+h3W5y+JcaWwU9CIiZRQXlUBCPbxkGwBZp2YA0CarC7+6/xF6DryG1d98xTO33ciMp//B9s2b41lupRT0IiJlJCaFaN72CEqKVuDurFq84cd9CaEQx5x9LleOGEXWaT9n7tRXefKWwcx7/TVKSorjV3QFFPQiIlG07XIU+Ba8ZAMzn1/M6m837rI/Ne1ATr/qBvoPH0HTjJ/xxuhHGXvXUHK/mB+nisunoBcRieKIE44BIBRaRf2GSbw+5ovwlE4ZzTLbcfEf/kKfW+5g+6ZNTPjfO/nviP8jf220i+7Fh4JeRCSKgzJak5TSkIItS2l/XAvy87axNT/60koz4/ATejDoocc54ZeX8m3ObMYMvY73n3+WHYUFtVz57hT0IiJRmBmNDz6UkqIVfDJtKaHEBFLTkivsk1QvhRMvupxBDz1Bu2O68sELzzJm6LUs+mBmXJdjKuhFRMrRsOkhUJJPo/SvuPCOYwglxhaZaenNOHfonVz8P/eRknoAk0cM5/k/3U3e0u/2csXRKehFRMpx1nWXkNExm9VfTebTKU9TXLQjarvi/HxW/eGPrBvzNMWbt/y4vXWnLPoPH8lpv76evGVL+PcdQ3hj9GNs25RfW4cAVOHCI2YWAnKAFe7ex8z+CFzNT9eIvdvdp0TpNxS4ivBFwT8HBrn79opeSxceEZG6wktKmDVxHLNfmkBGh86c+5u7SE078Mf9JYWFLMo66sfHoaZNaTJoIKHGB1G4fBmJTZrSuN8lbN++jfcnjmPe9FdJMuP4bqdw3JBba6zOmrrwyBDClwMs7SF37xK5RQv5VsDNQLa7dwZChK8bKyKyT7CEBE7qN4DeN9/G6sVfMe7u35C3bMmP+7fPD18dNalVKzInTiA5I4M1f32AVXffzbrHn+D7e+9lxbDbSMY47cprOb1eIxrkb+G9999i9ph/1soxxBT0ZpYBnAOM3oPXSATqm1kikAqs3IPnEBGJqw7dT+GSPw6nuGgHz/3+NhbnzAYgpcMRAJQUFpCUkUGb557l0Hfe5pBpUzl83lyaDbuVTdOn882ZP2fNyJE075zFcd+spMWGzcyc+h/mvf7aXq891hH9COB2oOwi0hvN7DMze8rMGpft5O4rgAeAZcAqYKO7T4/2AmY22MxyzCwnLy8vWhMRkbhqcWh7Lr/vQQ5qmcF/HriX2S9NxFJSyHjicUryN7HklxdR+M03JDVvTnKbNiTUq0eTq64ic8J46nfpwron/sEPTz5FAtC1OIlmm7fzxpOP8cW7M/Zq3ZXO0ZtZH6C3u19vZqcCwyJz9M2BtYTn3u8BDnb3K8v0bQy8CFwCbACeB15w97EVvabm6EWkLttRWMC0x0ey6P136XDSqZx5zc3sWPQVy6+/Di8opO2LL5DcuvVu/QqXLWP9s8+BOw1/fibf9h9ATmZzfmiYyjm33MHhJ/TY45oqmqOPJej/AgwAioAUIA2Y5O79S7XJBCZH5uFL970IOMvdfx15/Cugm7tfX9FrKuhFpK5zdz56+Xlmjv8XLQ5tT99hvyN502a+Pa8vaWefTcu/3Ffpc2xbsIDv/zGKGd/MZ8MBKZz1i350vGzAHtVTrTdj3f0ud89w90zCb6TOcPf+ZnZwqWbnA9G+4GEZ0M3MUs3MgNPY/Q1dEZF9jplx/PkXc96w37Ju+TLG3T2U9UWFNLroIjb+978U5uZW+hz1O3WizcgRnHvldTQodma+NGGv1FqddfT3m9nnZvYZ0BMYCmBmLc1sCoC7zwZeAD4hvLQyARhVvZJFROqOw7qewKX3/JWEUIjxf7iDdccciYVC5D00IqZPw5oZTX9xPvUsgQSzvVJjzOvoa5OmbkRkX7N14wZeefA+Vnz5BUe2akvGlDc4aMAAWvz27kr7rp70IuPGP8UxXbrS8+4/7tHr19Q6ehERKUfqgY246Pd/pnPPM/h8xXd8fuIx5I0bS1EMqwi/eu9tMKPTZb/aK7Up6EVEakgoMYkzr7mZU391NSu25vPBoa1Y9VrF6+TzXp3M58sW04gE0tu03St1KehFRGqQmXHsOX05/84/sj2lHi//5znm/e1+Nrw2FS8q2qVtwXffMe2Rv1EQCnHmjbdimqMXEdm3rPlsHi/dczebE4ykomJaeIg2zQ+mw5WDaXDUUbx+yw3MX7eKk869kOP7D6rWa1VrHX08KOhFJCgKN+Xz9RvTWPzxhyxdvIgdZQbtbRo25sJRz2AJ1ZtgUdCLiNQBxUVFLJ87hxf/eg8Ap53Wm6yrriWhmiEPCnoRkcDT8koRkf2Ygl5EJOAU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8iEnAKehGRgKuTH5gyszxgabzrqEVNCV9/V3anc1M+nZvy7Y/npo27p0fbUSeDfn9jZjnlfaJtf6dzUz6dm/Lp3OxKUzciIgGnoBcRCTgFfd2gC6aXT+emfDo35dO5KUVz9CIiAacRvYhIwCnoRUQCTkEfJ2bWxcw+NLO5ZpZjZsdFtieZ2TNm9rmZLTSzu+Jda22r4NxcHtm281ZiZl3iXG6tKu/cRPZlmdkHZrYg8vuTEs9aa1sFvzeZZrat1O/NE/Gutda5u25xuAHTgbMj93sDb0fuXwaMj9xPBZYAmfGuty6cmzJtjgS+jXetdeXcAInAZ8BRkcdNgFC8660j5yYTmB/v+uJ5S9wbfzwkJg6kRe4fCKwstf0AM0sE6gOFQH7tlxdX5Z2b0i4Fnqu1iuqO8s7NmcBn7j4PwN3XxaG2eIvl92a/pFU3cWJmHYBpgBGeQjvR3ZeaWRLwb+A0wiP6oe6+Xy0VK+/clGnzDdDX3efHocS4qeD35hbgWKAZkE74f4X3x63QOKjg3GQCC4CvCA+afufu78Wt0DjQiH4vMrM3gBZRdv2WcJAPdfcXzexi4EngdOA4oBhoCTQG3jOzN9z921oqu1bs4bnZ2fd4YGtQQ34Pz00icBLQFdgKvBm5WPSbtVR2rdjDc7MK+Jm7rzOzY4GXzayTu+83/1PWiD5OzGwj0Mjd3cwM2OjuaWb2KPChu/870u4pYKq7T4xnvbWpvHNTav9DQJ673xe3IuOkgt+bfsBZ7j4w0u73wHZ3/2scy61Vlf3elGr3NjDM3XNqu8Z40aqb+FkJnBK53wv4OnJ/GdDLwg4AugFfxqG+eCrv3GBmCcBFwPg41FUXlHdupgFZZpYaeX/nFOCLONQXT1HPjZmlm1kocr8dcBgQqP8hV0ZTN/FzNTAy8o9yOzA4sv1RYAwwn/Bc4xh3/yw+JcZNeecG4GQgN2hTWVUQ9dy4+3ozexD4mPCbklPc/dX4lRkX5f3enAz8ycyKCE+LXuvuP8SpxrjQ1I2ISMBp6kZEJOAU9CIiAaegFxEJOAW9iEjAKehFRAJOQS8iEnAKehGRgPt/H2GDRdJJ+04AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#convex-hull these right away  (decision for each state - YES for MI)\n",
    "for m in range(nTracts):\n",
    "    if notPoly[m] == 1:\n",
    "        for geom in tractGeom[m].geoms :\n",
    "            xg,yg = geom.exterior.xy\n",
    "            plt.plot(xg,yg)\n",
    "        tractGeom[m] = tractGeom[m].convex_hull\n",
    "        x,y = tractGeom[m].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "        notPoly[m] = 0\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a05c0ec2-d208-45b8-8ff6-c1f1f583ad5d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[69, 114, 123, 151, 165, 173, 185, 187, 188, 189, 190, 209, 210, 265, 268, 269, 270, 283, 311, 317, 324, 328, 341, 351, 363, 372, 374, 377, 498, 509, 524, 540, 651, 684, 696, 967, 970, 971, 977, 980, 981, 983, 984, 985, 986, 988, 989, 990, 1073, 1077, 1083, 1084, 1087, 1088, 1090, 1155, 1181, 1182, 1183, 1226, 1243, 1250, 1317, 1366, 1367, 1403, 1445, 1508, 1578, 1604, 1605, 1629, 1687, 1750, 1758, 1764, 1868, 1880, 1919, 1940, 1951, 2161, 2177, 2346, 2399, 2408, 2456, 2465, 2467, 2496, 2573, 2585, 2603, 2634, 2668, 2685, 2706, 2707, 2708, 2712, 2722, 2742, 2757, 2874, 2886]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# For potential later issues, identify and flag lakeshore low-population tracts.\n",
    "isSkippedTract = [0]*nTracts\n",
    "lakeTracts = [9999]\n",
    "minTractPop = 20\n",
    "for m in range(nTracts):\n",
    "    if tractPop[m] < minTractPop:\n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        if y < 999 and x > -999 : #optional to exclude some areas from plotting\n",
    "            plotPoly(tractGeom[m])\n",
    "        #plt.scatter(x,y)\n",
    "        if x > -999 :            \n",
    "            plt.text(x, y+0.00,m, fontsize=9)\n",
    "            if lakeTracts == [9999]:\n",
    "                lakeTracts = [m]\n",
    "                #isSkippedTract[m] = 1\n",
    "            else:\n",
    "                if 0 ==0 :  #m==99940 or m==999909:\n",
    "                    lakeTracts.append(m)\n",
    "                    #isSkippedTract[m] = 1\n",
    "                else:\n",
    "                    thisTract = \"interior\"\n",
    "                    \n",
    "\n",
    "print(lakeTracts)  # assigned as skipped tracts as well\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da1f99a3-04d0-427b-95e3-d9a57836959d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "greatLakeTracts = [2403,2408, 1226, 185,188,1403,2346,509, 2177, 2757,2742,2707, 1155, 1578,190, 1243,69,\n",
    "        1367,1317,1868,2603,1940,1508,2585,524,1919,2668, 210, 2399, 2573, 1250, 151,2161, 2496, 2874 ]\n",
    "for gLT in range(len(greatLakeTracts)):\n",
    "    t = greatLakeTracts[gLT]\n",
    "    isSkippedTract[t]=1\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b0e9e246-a558-475c-8181-075a9421034e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3017 35\n"
     ]
    }
   ],
   "source": [
    "print(len(isSkippedTract),np.sum(isSkippedTract))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d63968a2-afc1-4913-863c-4c415fb9c0b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "looking for tracts with zero area\n"
     ]
    }
   ],
   "source": [
    "# For potential later issues, identify and visualize zero-Area tracts.  See Ohio code for more code\n",
    "#isSkippedTract = [0]*nTracts\n",
    "print(\"looking for tracts with zero area\")\n",
    "for m in range(nTracts):\n",
    "    if(tractArea[m] == 0):       \n",
    "        x = tractGeom[m].centroid.x\n",
    "        y = tractGeom[m].centroid.y\n",
    "        print( m,tractPop[m],\"(\",x,\",\",y,\")\" )\n",
    "        x2,y2 = tractGeom[m].exterior.xy\n",
    "        plt.plot(x2,y2,c=\"purple\")\n",
    "        #print(tractGeom[m])\n",
    "        plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "72abde7f-085d-4c0a-abe3-29e8d6203b40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PRECINCTID</th>\n",
       "      <th>COUNTYFIPS</th>\n",
       "      <th>cousubname</th>\n",
       "      <th>elexpre</th>\n",
       "      <th>G20PRERTRU</th>\n",
       "      <th>G20PREDBID</th>\n",
       "      <th>G20PRELJOR</th>\n",
       "      <th>G20PREGHAW</th>\n",
       "      <th>G20PRENDEL</th>\n",
       "      <th>G20PRETBLA</th>\n",
       "      <th>G20USSRJAM</th>\n",
       "      <th>G20USSDPET</th>\n",
       "      <th>G20USSGSQU</th>\n",
       "      <th>G20USSNDER</th>\n",
       "      <th>G20USSTWIL</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WP-001-01040-00001</td>\n",
       "      <td>001</td>\n",
       "      <td>Alcona township</td>\n",
       "      <td>001-ALCONA TOWNSHIP-0-0001</td>\n",
       "      <td>564</td>\n",
       "      <td>248</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>539</td>\n",
       "      <td>267</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>POLYGON ((-83.29467 44.77346, -83.29577 44.773...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WP-001-12460-00001</td>\n",
       "      <td>001</td>\n",
       "      <td>Caledonia township</td>\n",
       "      <td>001-CALEDONIA TOWNSHIP-0-0001</td>\n",
       "      <td>508</td>\n",
       "      <td>245</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>485</td>\n",
       "      <td>261</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>POLYGON ((-83.64206 44.81382, -83.64578 44.813...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WP-001-19320-00001</td>\n",
       "      <td>001</td>\n",
       "      <td>Curtis township</td>\n",
       "      <td>001-CURTIS TOWNSHIP-0-0001</td>\n",
       "      <td>486</td>\n",
       "      <td>238</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>456</td>\n",
       "      <td>240</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>POLYGON ((-83.64530 44.51091, -83.64918 44.510...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WP-001-34820-00001</td>\n",
       "      <td>001</td>\n",
       "      <td>Greenbush township</td>\n",
       "      <td>001-GREENBUSH TOWNSHIP-0-0001</td>\n",
       "      <td>560</td>\n",
       "      <td>302</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>531</td>\n",
       "      <td>322</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-83.31858 44.51165, -83.32054 44.511...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WP-001-35740-00001</td>\n",
       "      <td>001</td>\n",
       "      <td>Gustin township</td>\n",
       "      <td>001-GUSTIN TOWNSHIP-0-0001</td>\n",
       "      <td>317</td>\n",
       "      <td>112</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "      <td>122</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>POLYGON ((-83.40227 44.59806, -83.41508 44.598...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           PRECINCTID COUNTYFIPS          cousubname  \\\n",
       "0  WP-001-01040-00001        001     Alcona township   \n",
       "1  WP-001-12460-00001        001  Caledonia township   \n",
       "2  WP-001-19320-00001        001     Curtis township   \n",
       "3  WP-001-34820-00001        001  Greenbush township   \n",
       "4  WP-001-35740-00001        001     Gustin township   \n",
       "\n",
       "                         elexpre  G20PRERTRU  G20PREDBID  G20PRELJOR  \\\n",
       "0     001-ALCONA TOWNSHIP-0-0001         564         248           3   \n",
       "1  001-CALEDONIA TOWNSHIP-0-0001         508         245           4   \n",
       "2     001-CURTIS TOWNSHIP-0-0001         486         238           2   \n",
       "3  001-GREENBUSH TOWNSHIP-0-0001         560         302           9   \n",
       "4     001-GUSTIN TOWNSHIP-0-0001         317         112           9   \n",
       "\n",
       "   G20PREGHAW  G20PRENDEL  G20PRETBLA  G20USSRJAM  G20USSDPET  G20USSGSQU  \\\n",
       "0           2           0           2         539         267           4   \n",
       "1           0           0           0         485         261           1   \n",
       "2           1           0           1         456         240           5   \n",
       "3           1           0           1         531         322           4   \n",
       "4           0           0           0         306         122           1   \n",
       "\n",
       "   G20USSNDER  G20USSTWIL                                           geometry  \n",
       "0           2           3  POLYGON ((-83.29467 44.77346, -83.29577 44.773...  \n",
       "1           0           4  POLYGON ((-83.64206 44.81382, -83.64578 44.813...  \n",
       "2           4          10  POLYGON ((-83.64530 44.51091, -83.64918 44.510...  \n",
       "3           5           6  POLYGON ((-83.31858 44.51165, -83.32054 44.511...  \n",
       "4           0           6  POLYGON ((-83.40227 44.59806, -83.41508 44.598...  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now, let's read in the voting data  \n",
    "VTDdbf = gpd.read_file(\"state_map_files/mi_vest_20.dbf\")\n",
    "VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4f3b9ddb-b971-472c-a353-521377f95e46",
   "metadata": {},
   "outputs": [],
   "source": [
    "#VTDdbf = VTDdbf.to_crs(tractPopFile.crs)  #MD's precinct file in wrong CRS\n",
    "#VTDdbf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3fe4ceba-8077-4485-ad0a-f8f2a5755040",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4756 0.48586542278500044 5453895 = number of precincts, statewide GOP vote, total Trump+Biden votes\n"
     ]
    }
   ],
   "source": [
    "# Pull VTD geopandas data columns into arrays\n",
    "vtdGeom = VTDdbf['geometry'] \n",
    "# vtdGeom = VTDdbf['geometry'] #can't use VTDdbf because it uses alternate coordinate system\n",
    "vtdTrump = VTDdbf['G20PRERTRU']\n",
    "vtdBiden = VTDdbf['G20PREDBID']\n",
    "\n",
    "nPrecincts = len(vtdGeom)\n",
    "stateGOP = np.sum(vtdTrump)/(np.sum(vtdTrump) + np.sum(vtdBiden) ) \n",
    "print(nPrecincts, stateGOP, np.sum(vtdTrump) + np.sum(vtdBiden),\n",
    "      \"= number of precincts, statewide GOP vote, total Trump+Biden votes\" )\n",
    "vtdTrump = vtdTrump.to_numpy()  #these two lines try to avoid pandas complaints when we overwrite precinct data\n",
    "vtdBiden = vtdBiden.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c5c57f52-b8d2-4ef4-9d02-2429ed5e59da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ID the non-polygon PRECINCTS\n",
    "notPolyVTD = [0]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    if type(vtdGeom[p]) != type(vtdGeom[0]):\n",
    "        notPolyVTD[p] = 1\n",
    "        plotPoly(vtdGeom[p])\n",
    "        #plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,p)\n",
    "    else:\n",
    "        hi = \"hi\"\n",
    "        #x,y = vtdGeom[p].exterior.xy\n",
    "        #plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ea498c6a-d054-409e-92fb-cda921e2911f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#prep for geography triage\n",
    "#isSkippedTract = [0]*nTracts  #done above; already skipped two lakeshore tracts\n",
    "isSkippedPrecinct = [0]*nPrecincts  #will be used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3ded1b1f-5629-4d8f-826b-e037e5dcb3da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 300\n",
      "working on tract 600\n",
      "working on tract 900\n",
      "working on tract 1200\n",
      "working on tract 1500\n",
      "working on tract 1800\n",
      "working on tract 2100\n",
      "working on tract 2400\n",
      "working on tract 2700\n",
      "working on tract 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#build basic MI map\n",
    "#tractMAP = tractGeom[0] #uncomment for first time thru loop  **************\n",
    "for t in range(1,nTracts):\n",
    "    if t%300 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        uncomment = \"if-redone\"\n",
    "        #tractMAP = tractMAP.union(tractGeom[t])  #uncomment to build map  ********\n",
    "plotPoly(tractMAP)\n",
    "#Let's draw a line to put all of MI's UP into LP's northern tip\n",
    "pt1 = Point(-88,44)\n",
    "pt2 = Point(-83,46)\n",
    "pt3 = Point(-88,49.5)\n",
    "pt4 = Point(-91,46.4)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #in case we want to clip southern tip\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()\n",
    "wholeMAP = tractMAP  #in case we later slice parts out\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f33de347-5473-4b11-b3ae-86691b33098d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  164 tracts w pop 389281.0  to reassign to tracts...\n",
      "[24, 76, 90, 96, 98, 231, 504, 1412, 1428, 1467, 1468, 1470, 1471, 1596, 1597, 1663, 1669, 1873, 1878, 1920]\n",
      "I have flagged  303 precincts to reassign to precincts...\n",
      "[85, 88, 89, 90, 91, 92, 95, 98, 192, 193, 194, 196, 197, 198, 199, 200, 201, 361, 371, 387, 396, 398, 400, 828, 833, 834, 835, 839, 840, 1632, 1633, 1639, 1641, 2991, 2993, 3091, 3098, 3099, 3100, 3101, 3105]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.15 :  # and tractGeom[t].centroid.x > -76.4 :\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :  #As of 3/19/22 - Virginia -- if ANY part of precinct intersects\n",
    "        isSkippedPrecinct[p] = 1  #then it's a skipped precinct, so we have no offmap precinct parts\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.15 : # and vtdGeom[p].centroid.x > -76.4 :\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33897c0d-3254-4a76-9b82-314a8f320eaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e2595c62-579a-4af1-9552-0859524782be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "9e1d7fcd-cdeb-4317-9f89-5291a9595587",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "d2622568-1fc9-4089-9ae4-2550ea6fbaaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  389281.0 people (VAP of 318353.0 ) and  231974.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe47f683-719d-4a02-9648-905bddbf28a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for E corner / thumb\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "pt1 = Point(-82.5,44.2)\n",
    "pt2 = Point(-83.4,44)\n",
    "pt3 = Point(-83.7,43.7)\n",
    "pt4 = Point(-82.3,43.1)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "cac3531a-35c7-438d-b5b9-5fcdf0745329",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  27 tracts w pop 69748.0  to reassign to tracts...\n",
      "[344, 564, 1323, 1362, 1363, 1613, 1831, 2716, 2726, 2844]\n",
      "I have flagged  66 precincts to reassign to precincts...\n",
      "[3206, 3207, 3223, 3224, 3227, 3228, 3291, 3293, 3297, 3299, 3305, 3357, 3358, 3360, 3366, 3368, 3371, 3379, 4638]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1 and tractGeom[t].centroid.y < 43.6:  #near thumb, not finger:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "plotPoly(clipPoly)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 and vtdGeom[p].centroid.y < 43.6:  #near thumb, not index finger\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d7009c9a-d5ff-4570-855d-1fb439afc32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        if notPolyVTD[p]==1:\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "        else:\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0218d8e6-7832-479f-b44f-b37c117ee40c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    if notPolyVTD[p]==1:\n",
    "        for geom in vtdGeom[p].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = vtdGeom[p].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5d698e51-7b2d-40b8-b932-d22f20f09bb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    if notPoly[t]==1:\n",
    "        for geom in tractGeom[t].geoms:\n",
    "            x,y = geom.exterior.xy\n",
    "            plt.plot(x,y)\n",
    "    else:\n",
    "        x,y = tractGeom[t].exterior.xy\n",
    "        plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c17c1582-29d1-41bc-8f56-b32ac70747ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  69748.0 people (VAP of 55711.0 ) and  44132.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "55419803-eaaf-4181-8848-d8fdad0205b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Now, repeat for southwestern corner\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "pt1 = Point(-87.,42.5)\n",
    "pt2 = Point(-87,41.6)\n",
    "pt3 = Point(-85.8, 41.6)\n",
    "pt4 = Point(-86.2, 42.6)\n",
    "clipPoly = Polygon([pt1,pt2,pt3,pt4])  #LEFTOVER FROM INDIANA. plan to clip off SW corner near Evansville\n",
    "xc,yc = clipPoly.exterior.xy\n",
    "plt.plot(xc,yc)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "2a56b3e5-803d-425d-8bb6-abfba9aa9716",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have flagged  78 tracts w pop 232556.0  to reassign to tracts...\n",
      "[102, 212, 227, 228, 380, 593, 1483, 1491, 1492, 1497, 2432, 2513]\n",
      "I have flagged  103 precincts to reassign to precincts...\n",
      "[27, 31, 32, 44, 45, 49, 59, 346, 349, 3268, 3391, 3403, 3404, 3412]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find all tracts and precincts centered in this clipPoly, flag for cutting\n",
    "# and, ID all nearby tracts and precincts to receive their pops and voters\n",
    "cutTractList = [-99999]\n",
    "cutPrecinctList = [-99999]\n",
    "tractReceivers = [-88888]\n",
    "precinctReceivers = [-88888]\n",
    "popToCut = 0.\n",
    "for t in range(nTracts):\n",
    "    x = tractGeom[t].centroid.x\n",
    "    y = tractGeom[t].centroid.y\n",
    "    if tractGeom[t].intersects(clipPoly) :\n",
    "        CP = Point(x,y)\n",
    "        if CP.intersects(clipPoly):\n",
    "            isSkippedTract[t] = 1\n",
    "            popToCut += tractPop[t]\n",
    "            if cutTractList == [-99999]:\n",
    "                cutTractList = [t]\n",
    "            else:\n",
    "                cutTractList.append(t)\n",
    "            plotPoly(tractGeom[t])\n",
    "    else:\n",
    "        if tractGeom[t].distance(clipPoly) < 0.1: # and tractGeom[t].centroid.x < -76.5:\n",
    "            if tractReceivers == [-88888]:\n",
    "                tractReceivers = [t]\n",
    "            else:\n",
    "                tractReceivers.append(t)\n",
    "xp,yp = clipPoly.exterior.xy\n",
    "plt.plot(xp,yp)\n",
    "\n",
    "for p in range(nPrecincts):\n",
    "    x = vtdGeom[p].centroid.x\n",
    "    y = vtdGeom[p].centroid.y\n",
    "    if vtdGeom[p].intersects(clipPoly) :\n",
    "        isSkippedPrecinct[p] = 1\n",
    "        if cutPrecinctList == [-99999]:\n",
    "            cutPrecinctList = [p]\n",
    "        else:\n",
    "            cutPrecinctList.append(p)\n",
    "    else:\n",
    "        if vtdGeom[p].distance(clipPoly) < 0.1 : # and vtdGeom[p].centroid.x < -76.5:\n",
    "            if precinctReceivers == [-88888]:\n",
    "                precinctReceivers = [p]\n",
    "            else:\n",
    "                precinctReceivers.append(p)\n",
    "print(\"I have flagged \",len(cutTractList),\"tracts w pop\",popToCut,\" to reassign to tracts...\")\n",
    "print(tractReceivers)\n",
    "print(\"I have flagged \",len(cutPrecinctList),\"precincts to reassign to precincts...\")\n",
    "print(precinctReceivers)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "264c0a29-0366-433f-ae45-63b986566b34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 2 of 4\n",
    "#visualize the to-be-cut precincts, just to confirm there are really xx precincts up there\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] ==1:\n",
    "        plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "34f94200-4cc7-4226-9a6c-d6ae75b3380f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ge7XzGzKJviN4bReZCkr0M9j8hvRDyaDpH7rZ19FT4khEKoMS/QzW05dk+xs5P4tW0WqqwjTEIhq6ESkSJfoZLBIyuuJJdh8M3gPZpsZg/jYjMhWU6GewT5yb/kza83s6ShzJ9Fswq5pdAfwBJzIVlOhnsHn1VQC0BfB98iVza9kRwGErkamgRD+DzantT/TBm/flyLm17O+M09kbzAXSRYpJiX4Ga6yJYhbMRL9kbnopQfXqRSZvMrNXyhQLh4zG6igv7O3gsVdeH/P68cwMk3P1gJx1FnxhMS9jf+ZB7PY3ujh+wcgZPEWkcEr0M9yCWdXc98we7ntmT6lDKYnOUZZTFJHCKNHPcP/20beybTwzWBa4prYVeiGDi54U6daZOse+OhI2Tl48exy1ikguSvQz3MLZNSycXVPqMESkjOlhrIhIhVOiFxGpcEr0IiIVToleRKTCKdGLiFQ4JXoRkQqnRC8iUuGU6EVEKpwSvYhIhVOiFxGpcEr0IiIVToleRKTCKdGLiFQ4zV4pZS/Z2Yl3pxcSd/fM6iaZJU7cB1dZyWwPLrriOc+PWs+I87nrGXLevYB6csebs57M+fDcuVQvXz6h75kEixK9lLWeF17g1ff8ESSTpQ5l+plx3K9+SWTevFJHIjNcwYnezMLAJqDF3VeZ2VeBS4A48DJwpbu3FVJ20lGLZCTfOADJJHOv+AhVRx8N2OBKKcbAtln/8f5zmf2B3exyNqKenOcH6slxn4GFVQbvgVlh98mOb9h9+s8fevxx9v/LN0geOKBEL2MaT49+DbAF6F/Acz2w1t0TZvYVYC1wbYFlRYrCwunHTPXnnkvdGWeUOJrpk8oMVaW6tHi6jK2gh7Fmthi4GLi5/5i7P+Du/Qt6bgQWF1pWpGjCYQA8mSpxINMrVJNedUyJXgpR6Fs3NwJfAPL9a7oKuG+CZQEws6vNbJOZbWptbS0wLAk6yyR6ksFaRNxiMQA8Hi9xJFIOxkz0ZrYK2Ofum/Ocvx5IALeNt2w2d1/n7ivdfWVTU9PYkYsAhNOjjx6wh7EWjQKQUqKXAhQyRn8WsNrMLgKqgUYzu9XdLzezK4BVwPnugy+tFVK2WA2QYLNoJtEnAtajr6oC1KOXwozZo3f3te6+2N2bgcuAn2eS/IWkH76udvecA4X5yhYvfAm6gaGbwCb6vhJHIuVgMp+M/SbQAKw3s6fM7CYAM1toZj8tSnQiYxh4CBsKlzaQaaYevYzHuD4w5e4bgA2Z7aV5rtkFXDRaWZGiG3hvvfK4O32vvYb39aXbGQqRbDsIQOL1/SWOTsqBPhkrZc1CmQTvlft6ZefDD7Pz45/Iee7A92+n6c/+bJojknKjRC/lLZQZfUxVbqLvf/7Q9JnPULXkyPT8N4kEB++5l9nve1+Jg5NyoEQv5c3Sib6SPzAVnjMHgM4NGwjPnZueSiFkhBsbqDrm6BJHJ+VAiV7KWhCGbmLLllF7xumkDrbTt3t3+reXVJLeF18itmw51cuWlTpEmeGU6KW89U+BUMFDN+GGBo76zneGHPNEgudP/D08FawPisnEaOERKW+ZoRsqeOgmp4GpHwLWbpkQ9eilrFnIIFxF78vb6fz148Nesxxt+l9yTzfMsCmJs6cZHjblMLnqHLx42JTEjLivJxziKUK1kXSXK29sWUNU2OB2tFY9eimIEr2UNYtWU/8HX6HvjTra7u0pdTjTquHiG0l17yh1GFIGlOilrFl1PVZVR3SREZ07dHW/rL3B5fkYvpQgOa8h1zUOWPaSfwM1Dq1noFyO+2cdSnWHSHZEicztwUKerqf/mqENGagjO/a+XXOJLTtl+I1FRlCil7KWbOsFoPbNR9Pw9kUljmZ67fzif2GZeelFRqOHsVLWel9rByDRGrwFOMwY+luDSB5K9FLW6k49HIBIU22JIykBszGW8xFJU6KXsmZVmU/G9gbw7RP16KVASvRS1iwcgrDh8SAmesvxEFhkJCV6KXsWMjwVwIxn6SmMRcaiRC/lL2QQwERfwVPwS5Ep0Uv5M8OTwUv0gIZupCBK9FL2QrEw3hOsNWOB7NkWREalRC9lz6KhgL58YnrrRgqiRC/lL0QwE5569FIgJXopfxbMh7EihdJcN1L24j0JurceYPff/WbIccuedCxrv1/76z30HjOLcz95yjREOUX0800KoEQvZW9Pd5I5vUmq+0bOB5AvDzpwZFWIF19tn9LYppSGbqRASvRS9rbXVbHnsBpWr3nz+Mpd+0sOP6phiqKaDqYPTElBlOil7IUjIZKJ8Sc8B6pbOtm69tfDjqZlD/V4qgf3XkLZKz8NXjnkSO49K6DE8Csz+2Y5yhkhC/H0Q/dzznu1OLiMToleyl44GiLePf736A8dNQvf3z14IMcqhP0bB1u346kuGpvmjXK9MfCDInsJwdGuN2cwoadPef+prASfSiXpbDtAKBQiHI1S09hIy9bnaOl5iXMKbK8ElxK9lL10j3788/Wu+LOTC772/37q/9HVtos13/juuO8zWY/edTuP/MdtQ46tufVunviL+5l77FHTHo+UHyV6KXvhiJHM8SC2uPeoIpXqm5K69776Mg9/719JxHvZ/eJWAI55y1s5/ux3YmYjkjzATVdfTm/XIQ7s3jUlMUllUaKXsjfRHv247hGN4al4Uevsi/ey9+UXufN/Xjfi3CtPPM4rTzw+sD9vSTP7t28b2F9+5tn87sGfkegrbkxSmZTopexN9GHseERjMTw1sfl0UqkU3Z1dHDrQwaG2DrraO+k+2Mmme27hUNs+AMLRKJ+65T/o7min8/X9xOrqSCXTc+xbKMScBen1cLPH7Y976xmkUlpiSsZWcKI3szCwCWhx91Vm9lXgEiAOvAxc6e5tw8ocCfw7cATpRc/WufvXixS7CDA9PfporBpI8It/v4dUykn09tDX00VP1yHi3d309XTR19tNoreHRF8Pyb4eUsleUsle8DijfbKpccGHiFbPY8sjeznxHYuonzO3oJiaTzm1OI2TijeeHv0aYAvQmNlfD6x194SZfQVYC1w7rEwC+HN3f8LMGoDNZrbe3Z+bbOAi/aYj0fe/bfPET76d42wUC8UIhWOEIjEi0WqiDY1EY9VEY7VU1dRQVVNLdV0tsbo6aurrqGmsp/OA09VeTTga4+UnWnnhsT1EY2GSiRTJvhSJvlT6U7/tcZJJp352jEhV+g2j3q4E3R191M+JcfYH9XqljK6gRG9mi4GLgRuAzwG4+wNZl2wE3je8nLvvBnZntjvMbAuwCFCil6IJR23KE/3v/8n7WX7GqXgqSShsxGrTSbtudh3RWHTIkMpEvN7yOLtfPsjulw8OPWFQXRclHDa62uO4QyhixGoidHekHw6//QPHTfr+UtkK7dHfCHwByPcxwquAO0erwMyagTcDjxV4T5GChCIhUgnH3acs4YVCIZpPWjoldQOsXnMKnQd6iURDhKMhwpEQkaoQkWiIUDg992AymcJTTiQaBmDTT1/lsf98lVTKCYeV6CW/MRO9ma0C9rn7ZjM7N8f560kP0Yx8B2zwmnrgh8Bn3D3n5CJmdjVwNcCSJUsKiV0ESA/dAKQSTjhangmvui5KdV101GvC4RCEB/dDmXYn+1LpcyJ5FPJfx1nAajPbBtwBnGdmtwKY2RXAKuDDnmfSDTOLkk7yt7n7j/LdxN3XuftKd1/Z1NQ0zmZIkPUn+qkevplpItFgtlvGb8xE7+5r3X2xuzcDlwE/d/fLzexC0g9fV7t7V66ylv49+l+BLe7+T0WMW2RAf6JPTPGHpmaagR9wfZrYTEY3md/3vkl6zH69mT1lZjcBmNlCM/tp5pqzgD8m/VvAU5k/F00uZJGhwpH0cE3QerZB/U1Gxm9cH5hy9w3Ahsx2zidT7r4LuCiz/Ws0a7ZMsaAOYVgo/U/LtbqWjEFPcKTshTNvoQQu0asLJQVSopeyF44Ovn0SKP3TGmvxERmD5rqRshfJjFU//P2tRKsjgKf/78OSYPbmiNzoo5zLcTzPRfnLFnD9kEvGjqe3KzHqeZF+SvRS9g5bXM/iN80hEU+S7EthocHJv0IhGzLGMdpwx5BzQ1Z1yj6ee2do2XyX57756DHlrqx/M1YXZVZTTf4KRFCilwpQ21jFpZ8Z33qxIkGiMXoRkQqnRC8iUuGU6EVEKpwSvYhIhVOiFxGpcEr0IiIVToleRKTCKdGLiFQ4JXoRkQqnRC8iUuGU6EVEKpwSvYhIhVOiFxGpcEr0IiIVTtMUi5Sxf17/Aq+9fqjg663A9QcLXqVwHMsZWoEXjxXiXZt3ArDt7y8u/OYBp0QvUqb6kim+/tCLzKqJMrs2Oub1ha5E5RS+ZFXBdWoVrJJSohcpU5GQEQ4Zf3z6UVzzB8tLHc60ueEnz/G9ja+VOoyyojF6kTJlZtREw3TFk6UOZVo1VEfp6UvRlwzYYvCToEQvUsaiYQtcwquPpQciDvUmShxJ+VCiFyljkXCIRCpgib46neg7epToC6VEL1LGqsIh4olgPelsyPToO9WjL5gSvUgZi0VD9CaCNUbf36NXoi+cEr1IGYtFwvQmgjV001CdfpW0U0M3BVOiFyljsUiInr6A9egzQzcd6tEXTIlepIzFIqEA9uj7H8b2lTiS8lFwojezsJk9aWb3Zva/ambPm9nvzOxuM5udp9yFZrbVzF4ys+uKFLeIALFo8IZu+nv0Grop3Hh69GuALVn764ET3f0k4AVg7fACZhYG/jfwbmAF8CEzWzHxcEUkWywSojdgQze1VWHM9DB2PApK9Ga2GLgYuLn/mLs/4O793+mNwOIcRd8GvOTur7h7HLgDuHRyIYtIv+oA9ujNjPpYRO/Rj0OhPfobgS8A+f6Lugq4L8fxRcCOrP2dmWMiUgTp9+iDlegh/S69evSFGzPRm9kqYJ+7b85z/nogAdyW63SOYzk/3WFmV5vZJjPb1NraOlZYIgJURYx4wKZAgPQrlhqjL1whPfqzgNVmto300Mt5ZnYrgJldAawCPuyecyLSncCRWfuLgV25buLu69x9pbuvbGpqGkcTRIIrqD36+mr16MdjzGmK3X0tmQetZnYucI27X25mFwLXAue4e1ee4o8Dx5nZ0UALcBnw34oQt4gAJ3T8Fx9Nfo2WLw897jmW+fBhR4bv5+I5VgEZWW54vbkUcu9R6rGh5X6YfDVz4mDOu8lQk5mP/ptADFifWbVmo7t/3MwWAje7+0XunjCzTwL3A2Hg39z92UlHLSIAvLP+NZpCe9nU8PtZR/Ol2sHj/b9/W85rhx3L8ct67nK5yo4sN7xkzrpyDhBkHet6Ncd5yWdcid7dNwAbMttL81yzC7goa/+nwE8nHKGI5NU0uxGAlZ/5AYQC9PnHH/4J7Hy81FGUjQD9lyFSgcJV6a/J3tLGMd0iMUj0lDqKsqFEL1LOIrH010TQEn21Ev04KNGLlLOBHn3A5n2JVkOfEn2hlOhFyllgh24yPfqcD21lOCV6kXIW5KEbHJLxUkdSFpToRcrZQI8+YAkvUp3+qnH6gijRi5SzoPboo5lEr3H6gijRi5SzcCbRB61HH61Nf010lzaOMqFEL1LOIpmhm8D16GvSX/uU6AuhRC9SzoI6Rt/fo+/LN82WZFOiFylngU306tGPhxK9SDkL7MPY/h69En0hlOhFyllgH8b29+g1dFMIJXqRchbUh7ED79EHrN0TNJn56EWk1PoT3s+ug4f6Vx/JrNIxsGhIgfs5r2H0MgXvT6aOHOX75/bR0E1BlOhFylldE5z3RWjflTXvS+Zr3v3sY8P3C61jgvtjxjWOOuceA81vR8amRC9SzszgHZ8vdRQyw2mMXkSkwinRi4hUOCV6EZEKp0QvIlLhlOhFRCqcEr2ISIVTohcRqXBK9CIiFU6JXkSkwinRi4hUOCV6EZEKp0QvIlLhlOhFRCpcwYnezMJm9qSZ3ZvZf7+ZPWtmKTNbOUq5z2aue8bMbjez6mIELiIihRnPNMVrgC1AY2b/GeCPgG/nK2Bmi4BPAyvcvdvMfgBcBtwyoWhFRIBEKsENj93AG91vDBxzhs5rP3x/5O6wA+Nk2Qu2FMGGnRsAePqKp4taLxSY6M1sMXAxcAPwOQB335I5V8g9asysD6gFdk00WBERgN2du7nrhbs4ou4IZlXNGnE+V14qdmIulOMlu3e/Qnv0NwJfABrGU7m7t5jZPwLbgW7gAXd/YFwRiohk2d+9nzu33gnAp9/8aS459pISR1QcX/z1F3lsz2NTUveYY/RmtgrY5+6bx1u5mc0BLgWOBhYCdWZ2eZ5rrzazTWa2qbW1dby3EpEKl/IUj7Q8wjt/8E6++9x3OXbWsZzUdFKpwyoaxwlN0fsxhdR6FrDazLYBdwDnmdmtBdb/LuBVd2919z7gR8CZuS5093XuvtLdVzY1NRVYvYgEQVdfF5/9xWf50wf/FIDZsdnctfoujmo8qsSRFY+7FzIUPiFjDt24+1pgLYCZnQtc4+45e+U5bAdON7Na0kM35wObJhSpiARCX6qPnR07eeXgKzy7/1mee/05tryxhbbeNq5ZeQ3vPe691FfVlzrMopvKsfwJLw5uZu8BvgE0AT8xs6fc/Q/MbCFws7tf5O6PmdldwBNAAngSWFeMwEWk/Lk7D25/kN/u+y3b2rexrX0bOzt2kvQkAGELs3T2Us5YeAbvWfoeTltwWokjnjopT5WuR5/N3TcAGzLbdwN357hmF3BR1v6XgC9NJkgRqUwP73yYz234HABLZy9l2ZxlXHDUBTTPaqa5sZnj5hxHTaSmxFFOjxnZoxcRmSh35/7X7ufLj3wZgK+d8zUuaL6gxFGVmEPIpuZhrBK9iEy7+169j2t/de3Afnu8nXgynu7R2uA774YNDGdkb1eiFKkpq9vcJ/fpsKmwcuVK37RJz2xFKlFnvJMzbj9j0vUM/DAwG/KDodAfFKOVz1m2COX7r89VvrW7lQV1C/jxH/54Yt8Ps83unnM6GvXoRWRa1UZr+eypn+WXO3/JgZ4D7OvaR9KTfGTFR6gKV9Hf+fTM/zI7A9uOD70mq7M6cI37kOsHq/Gc9Q+vY0j5rO3sa8cqn2s7u45c7Tl9wenj+l4WSj16EZEKMFqPXtMUi4hUOCV6EZEKp0QvIlLhlOhFRCqcEr2ISIVTohcRqXBK9CIiFU6JXkSkws3ID0yZWSvwWgluPQ/YX4L7TjW1q3xUYptA7ZoOR7l7zlWbZmSiLxUz25Tvk2XlTO0qH5XYJlC7Sk1DNyIiFU6JXkSkwinRD1WpyxyqXeWjEtsEaldJaYxeRKTCqUcvIlLhApfozewUM9toZk+Z2SYze1vWuZPM7FEze9bMnjaz6hzlv2pmz5vZ78zsbjObPa0NyKMI7Xp/5nzKzGbEWwRFaNNcM1tvZi9mvs6Z3hbklq9dZtZsZt2Z40+Z2U15yp+cafvTZnaPmTVObwtyK0K78v59l1IR2nVn1jXbzOypaW0AZFY6CdAf4AHg3Znti4ANme0I8Dvg5Mz+YUA4R/kLgEhm+yvAV0rdpiK163hgObABWFnq9hSpTf8AXJfZvq4M/q6agWcKKP84cE5m+yrgb0rdpiK1K2f5Uv+ZbLuG1fU14K+muw2B69GTXlSsvwc0C9iV2b4A+J27/xbA3V939+SIwu4PuHsis7sRWDzF8RZqsu3a4u5bpyXSwk2qTcClwHcz298F/nDqQh2XfO0q1HLgl5nt9cB7ixTXZE22XZMtP1WKEpelF4v9AHB7keIqXKl/Wpbgp/PxwHZgB9BC+tNkAJ8BvgfcDzwBfKGAuu4BLi91m4rZLmZWj35SbQLahu0fKHWbxmhXM3AIeBJ4GDg7T/lHgEsz258DOkrdpiK1K2f5Uv+ZbLuy6nkHsKkkbSj1N3GK/mIeBJ7J8edS4F+A92au+wDwYGb7GuBV0h9prgUeBc4f5R7XA3eTeXOpgto1rYl+KttUykQ/wXbFgMMy26dmEktjjrrfRHo4YTPwJeD1CmlXzvLl3q6se3wL+PPpatOQe5fipqX8Axxk8LVSA9oz25cBt2Rd95fA5/PUcUUmudSWuj3FbFfm/AZmTo9+Um0CtgILMtsLgK2lbtNo7ZrI3wWwDPhNqdtUjHYVWr7c2pU5FwH2AotL0YYgjtHvAs7JbJ8HvJjZvh84ycxqzSySuea54YXN7ELgWmC1u3dNQ7yFmlS7ZqjJtuk/Sf9QJvP1x1MY63jkbJeZNZlZOLN9DHAc8MrwwmY2P/M1BHwRyPm2RwlMql35ys8Ak20XwLuA59195xTHmlupf1qW4Kfz20n/yvtb4DHg1KxzlwPPkv6V7R+yjt9M5ic18BLpX9Geyvy5qdRtKlK73gPsBHpJ9zzur4A2HQY8RPof5kPA3FK3abR2kX6o+mzm+BPAJXnatQZ4IfPn75nG4cMpblfev+9ybldm/xbg46Vqgz4ZKyJS4YI4dCMiEihK9CIiFU6JXkSkwinRi4hUOCV6EZEKp0QvIlLhlOhFRCqcEr2ISIX7/9X+B79NIXMVAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#clipPoly block 3 of 4\n",
    "#now visualize the receivers\n",
    "for pp in range(len(precinctReceivers)):\n",
    "    p = precinctReceivers[pp]\n",
    "    plotPoly(vtdGeom[p])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "9301c018-ac66-4a64-8efe-a839a349e53f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#now visualize the TRACT receivers\n",
    "for tt in range(len(tractReceivers)):\n",
    "    t = tractReceivers[tt]\n",
    "    plotPoly(tractGeom[t])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "e8d4b939-2d62-4d7a-a866-7283b3e8e383",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I have finished slicing  232556.0 people (VAP of 182036.0 ) and  129455.0 voters out.\n"
     ]
    }
   ],
   "source": [
    "#now, cut and redistribute\n",
    "cutPop = 0.\n",
    "cutVAP = 0.\n",
    "cutHisp = 0.\n",
    "cutBlack = 0.\n",
    "cutTrump = 0.\n",
    "cutBiden = 0.\n",
    "\n",
    "for ct in range(len(cutTractList)):\n",
    "    t = cutTractList[ct]\n",
    "    cutPop += tractPop[t]  #sum up the cut pops\n",
    "    cutVAP += tractVAP[t]\n",
    "    cutHisp += tractHisp[t]\n",
    "    cutBlack += tractBlack[t]\n",
    "    # Now we zero out the pops in the cut tracts\n",
    "    tractPop[t] = 0\n",
    "    tractVAP[t] = 0\n",
    "    tractHisp[t] = 0\n",
    "    tractBlack[t] = 0\n",
    "# Now distribute the pops among the receivers\n",
    "ntR = len(tractReceivers)\n",
    "NTR = float(ntR)\n",
    "for rt in range(ntR) :\n",
    "    t = tractReceivers[rt]\n",
    "    tractPop[t] += cutPop/NTR\n",
    "    tractVAP[t] += cutVAP/NTR\n",
    "    tractHisp[t] += cutHisp/NTR\n",
    "    tractBlack[t] += cutBlack/NTR\n",
    "\n",
    "#             now do the same for the precincts\n",
    "for cp in range(len(cutPrecinctList)):\n",
    "    p = cutPrecinctList[cp]\n",
    "    cutTrump += vtdTrump[p]\n",
    "    cutBiden += vtdBiden[p]\n",
    "        # Now we zero out the pops in the cut precincts\n",
    "    vtdTrump[p] = 0\n",
    "    vtdBiden[p] = 0\n",
    "\n",
    "# Now distribute the pops among the receivers\n",
    "npR = len(precinctReceivers)\n",
    "NPR = float(npR)\n",
    "for rp in range(npR) :\n",
    "    p = precinctReceivers[rp]\n",
    "    vtdTrump[p] += cutTrump/NPR\n",
    "    vtdBiden[p] += cutBiden/NPR\n",
    "print(\"I have finished slicing \",cutPop,\"people (VAP of\",cutVAP,\") and \",cutBiden+cutTrump,\"voters out.\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "84c2cf09-b3ea-4fc2-a55b-62985882836b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "working on tract 200\n",
      "working on tract 400\n",
      "working on tract 600\n",
      "working on tract 800\n",
      "working on tract 1000\n",
      "working on tract 1200\n",
      "working on tract 1400\n",
      "working on tract 1600\n",
      "working on tract 1800\n",
      "working on tract 2000\n",
      "working on tract 2200\n",
      "working on tract 2400\n",
      "working on tract 2600\n",
      "working on tract 2800\n",
      "working on tract 3000\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#rebuild tractMAP, excluding skipped / sliced tracts.  #NOT NEEDED FOR OHIO -- NO SLICED populous TRACTS\n",
    "counter = -1 #Watchout - low-number tracts may have been skipped\n",
    "found = \"no\"\n",
    "while found == \"no\":\n",
    "    counter +=1\n",
    "    if isSkippedTract[counter]==0:\n",
    "        starter = counter\n",
    "        found = \"yes\"\n",
    "\n",
    "tractMAP = tractGeom[starter]\n",
    "for t in range(starter+1,nTracts):\n",
    "    if t%200 == 0:\n",
    "        print(\"working on tract\",t)\n",
    "    if isSkippedTract[t] == 0:\n",
    "        tractMAP = tractMAP.union(tractGeom[t])\n",
    "\n",
    "plotPoly(tractMAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "650cf37d-e11a-4ed3-861b-3f3edc411422",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#What is our distribution of precinct's Trump votes by precinct Biden votes?\n",
    "fig, ax = plt.subplots()\n",
    "ax.set(xlabel=\"Biden votes in this precinct\", ylabel=\"Trump precinct votes\")\n",
    "plt.plot(vtdBiden, vtdTrump, marker='.',linestyle=\"none\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "d40408f3-31eb-4b8a-ad91-4e2eb5f422df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 0.6945812807881774 812\n",
      "100 0.7099391480730223 493\n",
      "200 0.5932725998598458 7135\n",
      "300 0.33807829181494664 562\n",
      "400 0.5883896359462119 6098\n",
      "500 0.0 0\n",
      "600 0.5396694214876033 1210\n",
      "700 0.18564845292955892 1519\n",
      "800 0.7100694444444444 576\n",
      "900 0.0 0\n",
      "1000 0.23904761904761904 1050\n",
      "1100 0.6346153846153846 1040\n",
      "1200 0.6740213523131673 1405\n",
      "1300 0.4132013201320132 1515\n",
      "1400 0.4358830146231721 1778\n",
      "1500 0.406380027739251 2163\n",
      "1600 0.693127962085308 1688\n",
      "1700 0.729525120440468 1453\n",
      "1800 0.5386904761904762 1680\n",
      "1900 0.6302228412256268 1436\n",
      "2000 0.6035805626598465 782\n",
      "2100 0.0 0\n",
      "2200 0.6097240473061761 761\n",
      "2300 0.5656167979002624 762\n",
      "2400 0.5181598062953995 1239\n",
      "2500 0.66748046875 2048\n",
      "2600 0.5716244002741604 1459\n",
      "2700 0.30502885408079145 1213\n",
      "2800 0.12003610108303249 1108\n",
      "2900 0.6273637374860956 1798\n",
      "3000 0.6962142197599261 2166\n",
      "3100 0.5805706745835395 6063\n",
      "3200 0.5627753303964758 908\n",
      "3300 0.0 0\n",
      "3400 0.0 0\n",
      "3500 0.059244126659857 979\n",
      "3600 0.19094922737306844 906\n",
      "3700 0.0547112462006079 329\n",
      "3800 0.04914933837429111 529\n",
      "3900 0.0491183879093199 794\n",
      "4000 0.042071197411003236 309\n",
      "4100 0.04521963824289406 774\n",
      "4200 0.10665137614678899 872\n",
      "4300 0.40591848129536573 1791\n",
      "4400 0.412483039348711 1474\n",
      "4500 0.6075268817204301 1116\n",
      "4600 0.6324159021406728 1635\n",
      "4700 0.44787644787644787 1295\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ALL TRIMS COMPLETE. let's convert the partisan data to the format we use for HD code\n",
    "# in this prototype, we only use 2020 presidential data, but this could be modified\n",
    "vtdGOP = [0.]*nPrecincts  #Each precinct's R/(R+D)\n",
    "vtdPop = [0.]*nPrecincts   #THIS IS VOTER POPULATION\n",
    "vtdArea = [0.]*nPrecincts\n",
    "vtdCPx = [0.]*nPrecincts   #these are centroid x and y of each precinct\n",
    "vtdCPy = [0.]*nPrecincts\n",
    "plotcolor = ['blue']*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    vtdPop[p] = vtdTrump[p] + vtdBiden[p] \n",
    "    vtdGOP[p] = vtdTrump[p]/max((vtdTrump[p] + vtdBiden[p]), 0.01)  #avoid divide by zero\n",
    "    if (vtdGOP[p] > 0.40) :\n",
    "        plotcolor[p]='purple'\n",
    "        if (vtdGOP[p] > 0.60) :\n",
    "            plotcolor[p] = 'red'\n",
    "    vtdArea[p] = vtdGeom[p].area\n",
    "    vtdCPx[p] = vtdGeom[p].centroid.x  #not needed except for graphing\n",
    "    vtdCPy[p] = vtdGeom[p].centroid.y   #not needed  \"  \"  \"\n",
    "    if isSkippedPrecinct[p] == 0 and p%10 == 0: #memory drain if we print all\n",
    "        plt.scatter(vtdCPx[p], vtdCPy[p], marker='.',c=plotcolor[p])\n",
    "for p in range (nPrecincts):\n",
    "    if p%100 == 0:\n",
    "        print(p,vtdGOP[p],vtdPop[p])\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "a1e3f0d2-c21c-46d4-8227-e75dfd5e89b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "original and final map areas are 22.857894395881498 10.73403715291997\n"
     ]
    },
    {
     "data": {
      "image/png": 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OFoJSlOIgJkb+i3FxErO/7jqnLQpuSp3Yd+okWTtJSbBpk9Ts3rBBKmVu2CCLNpw86T0+OlrSzLKeCDK3smWdex+KUhro1w9mzoSWLZ22RCl1Yp9JVJSEclq3zt5urcTzM08CmSeCRYvg22+l7GsmderkfBKIifFmHCiKkjs9e8rtbbdBMeVmKPmk1GTj+IKUFNi8+ewTwYYNUlo5k4iInK8GmjbVhZwVJStut/w3tmyRK+3GjZ22qGSiqZd+wlqZUJL1JJC5bdkiP+hMqlfP+WqgQQNNB1WCk507ZYwsIgLuuQfuugvq1XPaqpKFin0AkJoqucgbN559Ijh0yHtcaKiksHXuDFdcIamiVao4Z7ei+JP58+HFF+HHHyUhYtEiaNs2+zHWynha+fLZ21NTYccOCc3WqROcqZwq9gHOsWPZxX/dOpg1C44elR98164wZAgMHaoLsSjBwR9/wCWXSIr0F194c/jXr5cCar//DjVqyInAWgn97NjhHUsrU0bCpZddJqttlSvn2FvxK34Ve2OMC1gM7LHWDsho+ydwH5AO/GStfTSvPoJN7HPC7Rav5pdfZNr5ihXS3rmz1BUfOlQLuimlF7cbXn8dnn9eBP3ee8Xrf+stEfK775YEiuXLJezZuLFsjRpJVc4NG6QA2/Tp4uWPGyf/mdLu7ftb7EcAHYDy1toBxpg+wJNAf2ttijGmurX2YF59qNifzebNkoP87beyahBIMamhQ+VEcOIE3HefTE7p2FH+EOCtLBgWJmmm8fFQq5Zjb0NRCsS774rQZ3LrrTB2rIx55Yf58+XEsHw5XH65rF7XqFGxmBoQ+ELssdaecwPqAn8AFwFTM9q+AS7Jz/Mzt/bt21sldzZvtvbFF61t395akfOzt0aNrP3zT2t79JD7VapYGxoq+1dcYe1//mPtsmXWJidbu2qVtSkpTr8rRTkbj8fahQvlN3roUOH6SEuzdtw4a8uVszYiwtrXX/epiQEFsNgWQGtz2vLl2RtjvgPGAuWAkVY8++XAZOByIDmjfVEOz70DuAMgJiam/Y4dO4p0cgoWNm6EefMkltmmjZR1Xr9eyjhnUq0a9Okjl7lxcXL8mZQvD+3aSQVDl0vin9deK4vBVKmis4fzw/r1Elpo0UKm/W/cCHv3QuXKUL++VHoMD/cev2CBXKUNGKBZJ/HxUhCtc2cpcdymjVyNpqRItdqUFAlb1q5d+N/ip5/CzTdL1tu6daUzpOOXMI4xZgDQz1p7jzGmN16xXw3MAB4AOgITgIY2jw41jFM0rIUJEyRsY638wDMnd7ndsG2bhHY+/VTim+HhMgi2b1/O/cXGSjXCq6+GgQNFvBRh0SJ4+GEpu3HsWN7HRkTIkn69e0ua4ZdfSnulSjIYH8x89hncdJP3fmioCPvBg2eveXvXXRKKDA+XhVAiIuQzPHhQ6lt16yYni/PO854Y3n9fwjktW8LUqaU30cFfYj8WGI4MwkYC5YHvgarAi9bauIzjtgAXWmsP5dKVir0DeDziZZ46Jfc3bRKv1Bi5Ssi8GqhQAQ4flj9jsHPrrbJKE4jA9Okjnum2bfK5tWwpg4NHj0rb0qXizf/1V/Y5F9dfLyfbYObtt+Gf/4RJk8SLX75cFjWpWlU+29RU+axPnJDfZk4n1qgouRo4cULuV6woop+WJr/fyy8XJ+jMlM3ShN9TL8/w7O8CaltrnzHGNEVi+jHq2Zcsjh715vtfeaVkCJXGy+D8cviwhMfat5crpILUdDl8WETN44HRoyU9MPOk4WueeUbCSRERMnhfoYKkMTZqJLfVqkltJ6fDdC+8AE8+KV58fpYldLtFxDPXpHC7JSnB7ZaEhbVrYc4cueJyuaBHD3juudLvpDhdz/5D4MOMcE4qcHNeQq8EJvv3i0Bs2SJpbYHKDz/A8eNw4415/7GPHPFerWSWtCgIzz4rt9dfX/DiXVWrynJ+IGJcnEI7YYKIfZ06Ero7dsy7NGAmxshJoE4dmcEdG5v9tkGD4i/vcfy4hGXyu/6syyVbZGT29tBQOQG3bw/Dh/vezmCgQGKfEbKJy9hPBW70vUmKPxk6VIT+4oul/v/q1eI9RUTAoEGB4eVPmSKTzwD27BFPMTcuuURCBSBivWbNufs/eVKOq1VLUgLr15f4cWFJTpaYc1JS4fvIi/h4+b42bpSYeJ8+cjWxZ4/M5t62Ta7Y4uPlJLB7t7TNmuUNhWRSo4aET4prctKJE3LCUZynlF/8KOfi6acl3/mPP84u33D++RKOcLlk9a927ZwR/8zIX/XqcgmfE+vWwVNPeYUe4N//PnffK1eKWGYdSO3QQUpfF5bMQfNKlQrfB4hQrllz9rZ3r/eYqCi5DQmRgc933pFQUoUKMmhcv75sF10kV3A7d4p9P/wgE/sOHJDBT1+KfeY40bRp8PPPpTuWXpJQsQ9yrr9ePPjp08WjDw8X0bj+eli1Sjz/TPr2lfBBUUWsoGzcKLdlyngnnmUlNRWuuip7GOqpp2Dw4Jz7S06W7KO4OO8KSn36SFZSjRpSu6gohIVJzDzrYG1upKXJCez88yUM5HZLiuemTdmPi4qS9osvlmyU886TQcqsqZ3Hj8viPZlZLgsXnvv1u3WT9+xLnnnGe6Jt314GaBXnUbFXKFNGBH/QIG/bsGHiRSYni6f2/ffwyiuSnlm3LixbJuJU3Pzwg5xgIiLE+8wcFTp5EsaPFzumTZP0vMGDxasFuSI5k7Vr4aWXICFBarCACPsDD3gXq/cVNWvKeEhOeDwi8F9+Cf/7n7Q9+qjYFhLiFfoXXvAKe2zsuddQqFwZliyBP/+Uq4JXX5UxjFdflZP26tUi7Fu3ynceFiYnD1+zbZv8RpYsyf+MWMUPFHVWVkE2nUFbslm2zDuTt0GD4nud7dutfe01a/v08b7eCy9Ye/XVst+unbVhYbIfE2PthRfKzOMWLaytXt3aAwdy7vf997393XKLzMAsLi691Npq1ax97DFrJ0ywduNGa5cssXbkSGvr1j17ZvTWrfK8tDRry5a19t57C//aCQnWTpxorTHS9xdf+OY95ZchQ6w9/3z/vmZpBx/MoFWxV/LNjh1ecRo5snheI6sgt2pl7T33WHv4sDx24IC1lSrJY7ffbu2CBd7nPf64tE+dmnvfaWnWNm8uJSY8nuKxP5MJE6xt29Z7UsrcQkOtHTBABPijj6Rt4kTv844elbaXXir4a7rd1t5/v7Uul/RRq5a1S5cW/3vN5PCpw/bzFZ/bhp3W2sgyKfZkotbq8BUq9orfGTNGfjXVqll78KBv+/7lF68oPvtszsckJFh75Ej2tr/+sjYkxNrbbsu7/8WLpe8xY3xibr5IThaPfvx4az/4wHvistbarl2lpsv+/d625cvFxoceKtjrJCZae9118ty//11OevHxvnkP+eWJ6U9YRmPp+pIFa6Ni1tof52z2rxGlFBV7xa/s3SuCembooaikplr73/9aW6aMtW3aSL/59UafeEI82Hr1zi1ugwdbW7Gi/0UwN5o3l8/x7rvlSmnAAPH8a9US0S8Iw4dLXy++WDy25odR00dZ13Muu/nIZjti3GxryhyyhCbaG56Y6ZxRpQQVe8WvfPmlV+j/+U/f9XvzzdJnmzbW7ttXsOdGRMhz587N+7gVK/K+YnCCX3+1tksXCU2Fh1vbuLG1I0YU7oqpWzdrO3TwvY0FYfSfoy2jsenudGuttSs2H7AVmi2zhKTaI8cTnTWuhOMLsddsHCXfDBsmaZCjR8vkJV+wcCF88gl06iQ1yguax28MPPKIrPqVF2PGSDbPAw8U3tbCsHKlrFVw5IisS5B1Vu5ll/kuC6hXL1n2b9eu4qu0mepO5Z6f7uFk6kkiQyMJDwnnxtY3cmHdC4kIjSAiVKbJpnnScIW4aN2oOg+O2MFzd4bRss9SzmubxKZ14YSGeyhT1k2Zspby5aFJwzAevaUV9Wvq7KviRMVeyTdJSVL/BUS8isrWrZLuGRsrE3wKM2HL7T53SuLGjbJAzKhR/pkjEB8vlUbHj5eVlIwROydPFjEujjIKt98uqbH33ScpnZmL3BQVay1u68ZlXGw7to0Pln1AjTI1CHOFsfvEbsYvG0+ZsDLcdsFt1CgrCfsp6SlEhkq9g6dua88fs+OY890FHFhagdAqO8CG4E6OxqaUBXcEvwPvjj5Bx4Fx/N+Y5rRrWtM3xivZKeqlQUE2DeOUXFatkrRGsPaqqyTOXlRat7a2QgVr164tfB+hodZGR4ttmdv550ssf9Eiif0/8ogcV9AQUWFp0cIb7rrzThlQHjFCMnOKMzPm1Vcl3bJWLdlfudLaY8ckS6cwjJ091tZ7rZ4Muo7Ghv4r1DIa+/6S96211s7ZMce+t/g92/StppbR2C7ju1hGYw8mnB2HOpmYYncdPH5W+/GEZPv+5FW2fvc5FpNucSXbhr1m2xuemGnf+ma5ff6DRXbrnmOFewOlCPy1eImv0KqXJZcHHoA335SZkU884W1PSZHwS6tWZ5dbyIsPPpCiYY88Ai+/XHi73n5bJktl/Rlv2CB1YNxuCWkcPy6Th77/vvCvUxAuuEDKNrzwglxNADz0kLznM2vTFJX4eLnaOnJEioft3y8hq6xlJXr0kM8jN+btmsf3674XUcCS7knnu7XfsS9BFkJ4ttezeKyHNHcaxhj+2emf1CrnXQNz1/FdtP1fW44mSc2JPSP2ULtc7QK/l7hlO7nvqe2smdEGkr0hHVel3fQZupkhl1dmeL/mlI0Kz6OX0onfliX01aaefclk926vp/rLLzLpadMmyaCpUUPay5fPO8c9k0OHZNINyKSprGmHvuTwYcljHzRI8urj4orndXJiyxa5uoiMFM/aWkmlLFvWd6+RnGzt6NHenPqsW7Nm2e83aZJ3XwO+HGDNaGPLvlDWln2hrC33QjkbNSbKPv7743br0fylXK0+sNqOmTnGjp091nqKePnidnvszGU77XPvL7QjX59no+qt9b6f8JM2rNrWjG2bdVXaZaNj1thXv1hapNcMdFDPXvEHL77o9VDPpH17ePBBWTUrMlI8zbCwnI9NSYF+/WDmTPF0X3gh92NLOrNmyaDp5MlSh+fhh6U0QkJC4fvcvl0WqJ8xQ+Lz6elSDXTQILmqWrVKjjlyRMYF2reX9l69ZOnK3Ljk00tITk9mzq25VJkLADbtOsqHkzcx5ecUTp2UQRprDbtXNMZag02szKi3FvPCvZ0dtrR4cLqevRIkjBghRbg+/lgqX9atK2ISHS3Fw955R+q99O6du3gnJ0uoZ8sWeO01EfvSTOfOUrxsxgwR+5CQ7KGm/GKtLL330UcSLsukd28p9Zw1K6p///z1ufHIRlbsX4HHevBYD7tO7KJe+cBeLLdJvcqMva8zY+87+7GDx05Rt/lOxt7Xka++mct7r9Shb6dYv9sY6KjYK+ckPPzsQmmZ/PyzVDW88kr4/POcn792rQjR9u2yOEhpF3qQwm3duonYg2TkFEbsH3pIKlm2aAHPPy8nkfLlJVW1MNlLRxKPcM2317DywMps7RfWvbDgnQUI1SuVYdWi8tz0yCwW/tCRS7tEcNMTs/nk+R5OmxZQqNgrhebgQa83+dlnOdct/+EHKZdcrpxUpBw40K8mOkqfPuJ9HzsmwpyeLnXzc1rY/dAhWXykTRuviB8+DG+9JQPZ771XtLUEjiQeYc2hNfT6uNfptrX3rCXEhBBiQoitGFv4zgOAZjFVWDChN40vmsOWP7vzx++h8LzTVgUWKvZKocms+z527NmrER07Bv/6l4hVx44i+jWDLH26aVO53bFDSgunpUmd+44dZTJVt24i6HFxst5tSgrcc49kGBkD69dLeGzIkHML/a7ju+j+UXfCXeHEVowl3BVOuCuchNQENh/dzPb47aePbV+rPXe2v5MW1VoU23t3irRUmcSwZ0EXYrrN49O369L7ghiHrQoMVOyVQnPsmNxmXSTEWnj9danP7nbDTTdJGKK41zr1NykpMjC9dausBNWypSxlmHXt1MxFQQ4elGO7dJHa+9OmSXqkxyOPR0SI+E+ZIssiPv64pIxmhn3yM4Htj21/sPP4TgCql6lOfHI8KekprDq4KttxF9S8gF9v/JWq0X5YjMAB4r5pScM6sr9rXleefzuO3h+o2IOKvVIERo2SeH2LFuJ9fvutZJw8/LA8vmCBeLGBsI6tr2ncWNZ2BVmgI3MQ9a675ORXq5Z3parQUBmg7dJFttGjJZyzbJn08eijIvQAd9/tLXfQvLnc5rSO7s7jOzmefPz0/fa12gPQqnor5t4693S7x3o4nHiYPSf2UCGyAg0rNfThpxB4NKhdkSfeXcAL90hWTnGtA1wS0dRLpUhkrhD111+S5pfphW7alHe6X0mncWNZDnH7dhHyKVNkgtjGjXJy69pVspV+/x3mzRORz4lnn5VwVyZut7ecgrUyQSsqCm54422enPEktcvVJj45nv0JOS+D1a1et4BOofQXa7YdolXLcELCk9ixOZq61Ur2Qrh+Tb00xriAxcAea+0AY8xo4HbgUMYhT1hrfy6KMUrJo3p1mDsXvv4afvtN0jNBRLC0ib21svh5fLzksH/zjSzkPX68DDxfeaVkHn3/PUycKJ8L5B3CGjHCK/a1a2evm2OMxPhPnoSvVn/FiZQTtKnRhi1Ht1C7XG0e6/YYdcrVweJ12DI9/GDD47G8+c0KfpsVT7UqLt54rCPvT9jM7YNbcF631Qy4+jjRkSHcMjiWbq3rOm2uI+TbszfGjAA6AOWziH2CtfY/+X0x9eyVkorHA9deKwXVcuKllyQck5UtW6R0Q79+efe9erUsON6kiXdx9Uzq1U+jwXmHmd1Zyg9MHz6dixsWw8KxJZwWl81i/W89T983ZQ7RvMc61v3WBTzZJ3/MXrGb7iVM8H3h2eer/p4xpi7QHxhflBdTlJLK5Mki9I0aSebMk09Ke2ZcffPms5/TqNHZQp+QmsD4peN57a/XTsfcGzYUj/6qIR5e/GQJV9wdx/gpqwHYcyiB2Ue8Z5jEtESfv7fSwPbVkuo17NFZ/OfzZdRovpV1cW3OEnoTfZQTp1KdMNFx8uXZG2O+A8YC5YCRWTz7W4ATSHjnYWvtsRyeewdwB0BMTEz7HTt2+Mx4RfEXW7ZIMbUdO6B1a5g9W+4fPSrhlmefheHDvcfP3jGbchHlOJFygi51uxDmEtHp90U/ftn8y+njNv1zEyd3NKZdu+yvZ6KOsXh5Cu2bV6Vu/8+Z+H8t6VSnkz/eaokkuv46kna2AONm14FT1K1WnnS3h98XbWfW4kPcMaQZDWpXdNrMQuMLz/6cYm+MGQD0s9beY4zpjVfsawCHAYtMX6hlrb01r740jKOUZJKSoEMHicv/9ZfMZs0p0+ho0lGqvOwtAdq8anNm3TKLMuFlKPPC2YXmW+9+i5XjpQ5ArY7z2bdIZrM27D2HrXHd+fr3DVx7SbPieVOlhBaXz2L9tJ7U7jSfDXFy5tyx/zjnNajmsGW+wV9hnG7AQGPMduBr4CJjzOfW2gPWWre11gO8D6jboZRqoqIkBz4sTLJrxo3L+bjVByUEc0XjKxh32TjWH17Pe0ve4+kZT5998L42p4Ue4K8pMtHJVWk3W+e2o1bH+Sr0+eCLN5sSVW89exdeSOXYPZSLDqdVw2pMnpVDfC1IOafYW2tHWWvrWmtjgWHADGvtjcaYWlkOuwpYXUw2KkrAMHy4pFNC7qteLdqzCIBfNv/Cg9MeBOCpP5/itfmvnT7GPmuxz1rqrX4z23NnLJbkffexuhCSzpRP6/v2DZRS2jWtScL2Ztz9wlzCy5w63T7imZxTVIORoiyQ9rIxZpUxZiXQBwiC8laK4h2UzW0hkmtbXcvzfZ5ncPPBp9tqlvXWiriu1XUAxCcks2uuN4MkrPo27vh72dP3R726jg7Ns/pUSl6EhBjeHdWNhK2tOJ6QAmGn2DqzO91vjHPatICgQGJvrY2z1g7I2B9urT3fWtvaWjvQWruveExUlMDiqCzIRHKKmzR3GumedLKOfdUtX5enej7FhwM/PN22+m7vhe/j3R8H4Ke52063VWyxjLSDDUg/LJ58n7/Hldra7P6gfJkI5iyWfJG5X/TG4/Hf5NFARcslKEoBWbDAAobHpj3JY4kvnW4PMSGEhoRm2zKpEl2F1KdSSUhNoFKUxH+27/WGG07s9i7jN/bjJYy80evxK4WjW+u61LnwLw5urUlISAOnzXEcFXtFyQOPxxK3bCeTZuzhp589HN1fhpQT5YFGNG0UyU19xuCxHtzWTbonHbdHbjO3MFcYPWKkrnqYK+y00AP0uKAG9brMY/BV8NajkoFTv8dcHr+5mxNvtVTiTg/Bul2kprkJD3M5bY6jqNgrSi78/dnZfPKfltjE+kB9TGQ80bV2Y8JSGPLQTL79z7OEhBS+ylvPtvXYOU+qnl13xR627z3JNRfnUkRHKRSXXp7Kp2NiuO6xmUx8rde5n1CKUbFXlBw4eOwUH/+7M9F1NjN0+Bqu6FmdQT0bER3Zqlher0urOnQpnq6Dmrq1wwFoGBPusCXOo2KvKDlwMjEV3FE0a3eIT8cEt0dYUlmz7RCvPFsdV6XdPHdXkeYjlQqKknqpKKWWRnUq0bD3PJZN7czyTQecNkcpBDc/soa0I/V4+e1DREeGnfsJpRwVe0XJhTfG1IL0SP7z8QanTVEKwebVFSkbu44R11/gtCkBgYq9ouTCkfgUAMJCS+FSW0FAaHg6ScdymeYchGjMXlFy4ZV3DwMQFqZiXxI5sr4FpJ1deC5YUc9eUXIhKVH+Hg/coIXIShqJyWmQVoYu18U5bUrAoGKvKLlw9EAZomLWlZoyucHE9MWybkbr8zR4kYmKvaLkQlh4Op40FYuSSFS4fG9H4t0OWxI4qNgrSg4cPZHEobXn0aTdXqdNUQrB7GUHAWhcP9JhSwIHFXtFOYOjJ5Lodf1CSIvmhmvKnvsJSsCQmJzGNY/MZMzDUvjsyds07TITvUZVlCx4PJbY9ps4ubkX5w+YyaPDtfpkSeH3hdu56tpTnNreC1yp1Gi3gLJRWiY6E/XsFSULgx6YycnNrbnszjhW/tirSIXOlOLlvUmrqNJqCcbIWsCXdo4l8WAtHnr1L2x6OPuXqNBnRT17Rcng01/WMvXt3lRsvoyp76hHH8hMmL6BO4e2IKTMkWztn31/gBsu08qhOaFiryjA5Fmbue3Gcpgyh1j+Z0NCXXrRG8iMGnMIE16DjevC2XNoF+99u40HbmxCxxYtnDYtYFGxV4KeybM2M7hvTUx4Eh9/e4j6NVs6bZKSB78v3M62md2p330ujep0o1GdSvRsW89pswIedV+UoObnv7YydFAUJiyJKb+e4KYrVOgDncfGyoSpf/w9wmFLShYq9kpQMmv5LppcPJv+3erjTirLF98fYUC3Rk6bpZyDxOQ0lk2S9QVuuLyxz/tPSIBDhyAx0eddO06+xd4Y4zLGLDPGTD2jfaQxxhpjqvrePEXxPU//dyG9OlZj86yOtL9qNqvXpXLdpc2dNks5BwlJqYz5YOnp+w3rVMRV7iAVmy+nzaCZfPfnxkL3vXs3PPsslCsH1atDmTLQqhW8/z7s3OkL652nIDH7B4B1QPnMBmNMPaAvUEo+DiUY+PhTN6RH8uOcLQzo1ttpc5RzsG1vPE1aJeA+VheQdMqrHpxJSqpl0wYX+3dUYOUvHblmSjTdb4xj9me9C9T/N9/ATTdBilS05v77IS0N5syBO+6QtkaNoE8fuPFG6NQJoqJ89/78Rb7E3hhTF+gP/BsYkeWh14FHgcm+N01Riof774ng0YVp3HTHcXYuTqVslK5PGgis3nqISXE7qFUtkvR0yx/zjrJzl+VUgsF9TEI3fW+P4/cPurFsaSiDBxpqVHNzvPUxpn61j9T9jVi34uwZz6tWwfbtEp45dAgOH5a2GTMgLExCNm3awNix0KULVKwoz/N4YPVq+PNPOfbrr2H8eKhTByZMgG7d/PfZ+ARr7Tk34DugPdAbmJrRNhB4I2N/O1A1l+feASwGFsfExFhFCQTuHDPbgrWd/van06YoNuP7CDtlwea63ffiXGuttXU6zzvrMVfF3faakXH2yPHEbP0+99zZ/URFWdu0qbV3323tiBHWvvWWtUlJ57bx8GFrP/rI2thYa8uVs3bJkmL4IHIBWGzzodV5bUb6yR1jzACgn7X2HmNMb2Ak8DfgT+BSa+1xY8x2oIO19nBefXXo0MEuXry44GckRSkGYrrNY/eyFiQcLatrlDrIXf+ew/+e6k7ZRqt44qlUUtM8APTpWJP6Ncvz0keraVw/+vTygnsPn2Ta/J1ULBdOtUpRVCkfSaM6lVi/zsXkyZCUJF759OmwZAkMHy6hmWrVoGpViccXhd27xatPTISFC6FBg6J+AufGGLPEWlu0VdPPdTYAxgK7Ee99P5AITAQOZrRtB9KRuH3NvPpq37598Z7+FKUAjHhNPMQPf1zttClBy/MfLDrtcZ9MTCl0P59+6vXcw8Jka9PG2nfesdbt9p29maxfb22lStY2b25tQoLv+z8TfODZnzMbx1o7ylpb11obCwwDZlhrh1prq1trYzPadwPtrLX7i3TmURQ/0r2dJJBNn3fkHEcqxUV4mEjQ4AdmFnrs5JVXZIAVxINPTZVt+XK45x4IKYYE82bN4PPPYf16yeDZWwIqYWuevRK0DOohedo/flvRWUOCmP2HJAXm/BaFS28ZMwYefRSGDROBf+MNX1qXN/36waefyvXEvff673ULS4HE3lobZ60dkEN7rD1HvF5RAo0d+48DkHy8nMOWBC8fvici3+G8yoV6/uuvw+WXi5cd5sCwy/DhcNdd8NNPsHat/1+/IKhnrwQtwx5aDsDLbx531pAgZc7K3ZzcUxtXxb0M7F7w2bDWijffvDm4XMVgYD4ZOVLy8vv3d86G/KBirwQdyanp1LhgIQu/6U3zS2fx4LC2TpsUlNz5+FY8CdW56JqCz3xdtAiaNpXyBuPGwbFjvrcvvzRqBN27w8GDztmQH1TslaBix/7j1Gi1hoPLO1Gu8UqW/djVaZOCljf/VR8iTrB0fllS0/K3MLi18M5/0+neHU6elLbo6OIZhC0I1op3H8io2CtBRZeB6zixtQV3jpnDiU2tiQzXKt9OcXGH+lzzz2UcWdWBcjHbqX7BIoyBXjfHkZh8tnIeO5HCeX0Xcd89oXTucYo1aySn/uhRqFDBgTeQhQ0bAl/szzmpypfopCrFSTweiys8jdb9/2LF5F5Om6Mg38nIcfN5/92yJGw53/tA+EmqtVhPt96JjLy1EZt3neCuO0NJ3tMYeo9m+vg+XNyoj3OGn0Hr1lCvngzUFge+mFSlnr0SNKS7PeBKJemU/uwDhZAQw2sjunBy8/m43ZaNO4/yyBvzOf/SpRzfX5lJb/Sie5u63DKgJSnxlbnuxc+g9/OkepKdNj0btWvDmjVOW5E3+qtXgobwMBctL17Kpj+78MGPq502RzmDkBBDk3qVefn+C1n5Yy9S9jfitwXbGXR/HNc9PovtW8J45KbWADz151Nc9MlFmOcMl3x6CW8ueNNR288/H3btgn37HDUjTzSMowQVO/Yfp1HLk9i0cH6PS+Ki9vWdNkkpAKdST3HdxOvYn7CfRXsXnW6vGl2Vp3s+TWzFWLrW6yolAhBty9zP1LrM/aiwKCpHFS6//0w2bpRZtc8/D0895ZMus+GLMI6KvRJ0/PzXVgZcUpGoqvs5sqmpDtKWUE6knOBEygm+XPUlj01/rFB9LLp9ER1qF62+WCaXXQbLlsliJ5GRPunyNCr2ilJIHvjPPN58pCv/eG4O7z/T3WlzlCKSnJ7MlqNbmL51OiEmBGMMBnP6FsjWtu/kPkbPHM2313zL1S2v9okNkybBVVfBu+/C3Xf7pMvTqNgrSiHxeCxlYzeQnhxBwp4YwsMcnIKp+J1tx7bR8M2GjOo+iosbXEyaJ400d9rp21R3KmmeNAyGoS2HUj6i/Dn79HigRQuZXLVvn2+9e1+IvV6/KkFJSIjh3hHH+M9DXRg5Trx8JXioEl0Fl3Exds5Yxs4Zm+exiWmJ3Nvp3JXOQkJk2cJnnoE9e2RmbSChYq8ELWP/2Zm3XtrMu2NjuPfaIzSLqeK0SYqfKB9RniV3LOFQ4iHCQsIId4UT5gojLCTs9K0rxEWjNxtxODH/NR7btJFbJ8s35IaKvRK0hLpCePO/Sdw5pAHX3DuHlT/qRKtgok3NNuc8pmx4Wf67+L9M3jAZt3Xj9riz3VpreabXM9zS9hYA6taV5+3eDR18M+7rMzTPXglq7hh8Pq2vnMOqqb0Y86GOJynZebjLw1xQ6wJql6tNbMVYmlVtxvnVz6d97fZ0qduF4ynHmbBmwunjs4p9oKGevRL0/PFpR+qdt4Fn7mtE51bb6dsp1mmTlABhdO/ReT4e9e8oft386+n7VatCeHhgir169krQU7VCND9NjsSmlOX62w85bY5SgmhVvVW2+yEhUKeOzKYNNFTsFQW4qH19ug6by+GVHbnr33OcNkcpIbSu3pra5Wpna2vbFqZOhUMB5jdoGEdRMvjjo+7UWrqM/41uzxU9NjOoZ8FXT1KCi2plqrE/YT/dP+xOqjuVFHcK8fVbc+LEZ1Sv4cF6AsefDhxLFMVhIsNDmf1TXUx4IjfdfgqPx38TDpWSyZVNr6RHTA8iQiOoEl2FBhUb0KFjxkIsNiSgCu7pDFpFOYNbnpnNJ8/34Pbn5/DeU1pKQSk4+48mULteKlWbbGH/0g6EhJgi9efXevbGGJcxZpkxZmrG/eeNMSuNMcuNMb8ZY2qfqw9FKQmMf7YbZRutYvyLzdi2N95pc5QSSM3KZel/60oOrejI8KdmE5/gfP39goRxHgDWZbn/irW2tbW2LTAVeMaXhimKU4S6Qvjgf+HYxMpceftKp81RSigTX+tBpZZL+XJsTyqViyRu2U5H7cmX2Btj6gL9gfGZbdbaE1kOKQNogFMpNfzt4ma0HTiHNT93Z/KszU6bo5RAwsNczJhU5/T9Vg2rOmhN/j37ccCjgCdrozHm38aYXcAN5OLZG2PuMMYsNsYsPhRouUiKkgcfvNwMCGHcRwE4Q0YpEWzbKz5xucYrqVoh2lFbzin2xpgBwEFr7ZIzH7PWPmmtrQd8AdyX0/Otte9ZaztYaztUq1atyAYrir9o17QmoVV3sGVDuNOmKCWQWct3cc1VURCSzhNPpTltTr48+27AQGPMduBr4CJjzOdnHPMlMNTHtimK45SvcZij+89dy1xRzuTep7biPlmVj6ds5PGb2zttzrnF3lo7ylpb11obCwwDZlhrbzTGNMly2EBgfTHZqCiOUaZ8CmmnnL38Vkom6+c3oGbbFdzcv6XTpgBFm0H7ojGmGRLH3wHc5RuTFCVwCAv34EkPc9oMpYSxZc8x0o9Xp0btbU6bcpoCib21Ng6Iy9jXsI1S6gkL9+BJ05i9UjDembAe0rtw9aAyTptyGi2XoCh5ULa8B8/JGgx7dLbTpigliOv7xYJx880Pp5w25TQq9oqSB0/fL6tRzJgW5bAlSkli4ZpDYF3UrhM4049U7BUlDwb1bExE7FJCbKTTpigBxLQF29h54Hiujz83Jhkij/Ply539aFXeqNgrSh5s2nmclD3NqF0/cC7HFWfYeeA4o95eQLnGq7j8wgZc++CyHI/71/hFHFzeif63LaNy+cC5ItR69oqSB4+8ugLSevLkI4Ez0Kb4j7krd9P9gpqEVtpD+pH6gNdTv3ZQxRyf89PvpyAkjW/+080/RuYT9ewVJQ8OHggB4+aLfc8yZMIQhkwYwpydupJVsPDc25vBE5oh9EKV86VM+98uaZTt2MTkNAY/EMfCSe0p13Ad0ZGBlbKrnr2i5EH63paEV9/OusNrCQ0JZfXB1dQsW5PuMVrnPhi4ul8Vfn8/e9uRVVJWPjoyjIlxG3nl//ZSpQrMmFSb5L29qdp6EVO+CLyK7yr2ipILaWmwenFl7rq9Mm/cK9W964+rT1J6ksOWKf7iP2/Hn9430UeJrr6PKnWOs3NuV4aPms/Ud7uBp6kcEH6Ska//xSsPdnHG2HOgYRxFyYV16yApCTpnSagId4WT6k51zijFbyzduJ9Nf/TgvH6zOJ6QgudUZRK2nUe1GvL9T327N7XaL2HjzqPMX72XXbttwAo9qGevKLny669ye+GF3jYV++AhKlzksVJlD+XLRJxuL5dRF6/v7XH8/N+ehLpKhs9cMqxUFD+zaxe8/jr07AkNG3rbw0LC+G7tdyzdt9Q54xS/kJSaLrdnRO2+erUDP87Zwm/v9S4xQg8q9oqSIzfeCPHx8Oab2dvrVagHwCO/P+J/oxS/0v/6HQB07Zy9NlLNymUZ0K1RTk8JaFTsFeUM5syBWbPgqaegTZvsj3006CMAWldv7YBlSnGTnJrOuxNXEtN1HvuXdCa08i7G3ON8LXpfoDF7RclCWhrcfz/UqQMPPnj24+Eu8fIW71vsX8OUYmHx+n28/MFG4qZHE7+3KmlH6oC7NYSk0+vmOCa9fWG2eH1JRsVeUbIwejQsWwYTJ0KZHCbNZor9nJ1z2J+wn5pla/rXQMVn3P3CXP7v2faQ3ouw6tuo3nA/dfvsoEvHcO66phnNYno7baJPUbFXlAx+/BHGjoVbb4UhQ3I+xmAAGajdHr+drce2smL/CjrV6UT72qXjcj8Y+GPxDv7vmQ5UaLyOLz8qT78uDYEGTptVrKjYKwowYwYMGwbt2589KJuV0JBQIkMjSU5PpssH3pzqylGVOfLoET9YqviCG+7cD6GVmDGlJu2aBsfVmYq9EtRYC6+9Bo8+Cs2bi3efU/gmE1eIi0W3L2LX8V2n227/8XbKR+ii5CWF5NR0Dqy4gHYD59GuaW+nzfEbKvZK0HLqFPzjH/D11zB0KHz0EZQrd+7ntareilbVW52+37VeV1YfXF2Mliq+ZO32w+CuSUyMcdoUv6Kpl0pQMm8edOgA33wjcfpvv82f0OdEaEgoaZ403xqoFBttG9fAVXEv82YFTq15f6CevRJ0pKRAt26SXvnrr9C3b9H6C3OFke5J941xSrHjsRawJJ0MrtXH8u3ZG2NcxphlxpipGfdfMcasN8asNMb8YIypWGxWKooPefZZue3fv+hCDxBqQklzq2dfUhjzwRLc8XUYfttJp03xKwUJ4zwArMty/3eglbW2NbARGOVLwxSlOHjnHXjpJYnRv/uub/oMDQlVz76EsHj9Pp67syMArzzYyWFr/Eu+wjjGmLpAf+DfwAgAa+1vWQ6ZD1ztc+sUxYdMnAj33QcDB8JXX4HL5Zt+w1xhnEw9ybj54063RYdFc1Obm4gMDa5QQSDi8VhGjpvPpCluti+QgfUmF88mOrKHw5b5l/zG7McBjwK5DWHdCkzI6QFjzB3AHQAxMTEFNE9RfIPHI7NjW7WSQdkwH64Y17BSQxLTEnlo2kPZ2uuUq0P/pv1990JKgdh/NIHv/tjCe5/Gs2pqL0z0EepesJYbrncx9r7gEnrIh9gbYwYAB621S4wxvXN4/EkgHfgip+dba98D3gPo0KGDLYqxilIYrIXrroPVq+GzzyDCx6VORnQZwW0X3IZFft7L9i3jok8v0tCOg/wwcxNDLo4Bt1SyM1HHSI6vSHhYV4ctc478ePbdgIHGmH5AJFDeGPO5tfZGY8zNwADgYmutCrkScOzcCbffDr/9BjfdBDfcUDyvUyGywun9zAlWIUYzm51gzsrdXHNVFLgjuO25OSxd5ubZh+oQHlbJadMc5Zxib60dRcbga4ZnPzJD6C8HHgN6WWsTi9NIRSko1sJ778HIkbL/zjtw111g/DCPxmM9gIq9v/F4LK36z2bdrz0BGPjPOMY/09tZowKIouTZvw1EAL8b+QfNt9be5ROrFKWQWAszZ0p65axZcPHFMH48xMb6zwYVe/8Tt2wnF/WIxp7qSav+M7np2nI8fEMvp80KKAok9tbaOCAuY79xMdijKIUiNRV++AHeegvmzoWaNcWz/8c//OPNZ8Vt3YCKvT8ZetMh7Kn21Os6jxVTehISElylEPKD8WeovUOHDnbxYl30QfEtixZBpzNSpjt1gqgcZsOfKfznul+YY44lHWXxvkUYE0LlqBzixFYOtmfc9z7u3T26th0AlVouy/mAfHIs6RhYQ7mICoQgOactLojnr696F7ivQKR620Uc212Dk3trExle+goDGGOWWGs7FKWP0vepKEFH1nz5nj0hJETCOWdibfb2M4/J7TkFPia9LBWoR7rHTcqpzFZDNpE+8wRypoCb7PfT07JfJRTYb3XLoivWbcAFxze0Zf7243k+xeOxpKa7SUxOIzEljaTkdJJS0723Kekkp7hlS5XblDQPScluklLcpKR6SE7xkJLiITnFkppmSUmxpKRaUlNlVbDUVNnSUg1paYb0jC0tLYT0tBDcaSGkp7twp7lwp7vwpLnwuF140kPxpIdi3WF40sOwCTJR6tn/zeelf15Y0E8nKFCxV0o87drlLMLOEQ609HGfbc59SAFofeVMVk3tRVjVHVh3qHfzhII7FDxhGSeI0IytmIqGhaSBKxVc6RhXKiY0nZDQNEJc6YSEZmxhblyhbsIjU3GV8xAa5iY01ENouIfQUEtYuCU0bC0REZZrL2tePHaWAlTsFSUIufuWSow9Og9XqCU01IPLZQkNg9BQS2iYJSyMs7bwMCNbuPc2Ijwk2xYV6SIiLIToyFCiIl1yGxFKdGQoZaLCKBMZRlREKGWjwomODCPUFQb4cIabkisq9ooShNw9tDV3D3XaCsWfaLqAoihKEKBiryiKEgSo2CuKogQBKvaKoihBgIq9oihKEKBiryiKEgSo2CuKogQBKvaKoihBgF8LoRljDgE7/PaC2akKHHbotfNC7SoYalfBULsKRqDa1cxam9uysPnCrzNorbXV/Pl6WTHGLC5q1bjiQO0qGGpXwVC7CkYg21XUPjSMoyiKEgSo2CuKogQBwST27zltQC6oXQVD7SoYalfBKLV2+XWAVlEURXGGYPLsFUVRghYVe0VRlCCg1Iu9MaaNMeYvY8wqY8yPxpjyWR4bZYzZbIzZYIy5zM92tTXGzDfGLDfGLDbGdMpoDzfGfJRh7wpjTO8AsCnMGPNJhk3rjDGj/GXTOey6IaMtc/MYY9o6bVfGY60zfndrMj63SKftMsbEGmOSsnxe/+cvm/KyK8vjMcaYBGPMyECwyxjTKctntcIYc1WA2NXXGLMk43e1xBhzUb46tNaW6g1YBPTK2L8VeD5jvyWwAogAGgBbAJcf7foNuCJjvx8Ql7F/L/BRxn51YAkQ4rBN1wNfZ+xHA9uBWKc/qzOOOR/Y6uffVm6fVyiwEmiTcb9KgPy2YoHV/vyMCvI9AhOBb4GRgWBXxm89NGO/FnAw877Ddl0A1M7YbwXsyU9/pd6zB5oBszL2fwcyF2MbhAhYirV2G7AZ6JTD84sLC2ReZVQA9mbstwT+ALDWHgTiAX9N8sjNJguUMcZkrjydCpzwk0152ZWV64Cv/GaRkJtdlwIrrbUrAKy1R6y17gCwy2lytcsYMxjYCqzxv1k522WtTbTWpme0R2YcFwh2LbPWZn52a4BIY0zEuXtz6Czvx7PjPGBQxv4I4GTG/tvAjVmO+wC42o92tQB2AruAPUD9jPY7EO8mFLniiAeGOmxTGPA1cAg4Bdzh5+8wR7vOOGYL0CoQ7AIeBD4DpgFLgUcDxK7YjO9vGTAT6BEgdpUB/gLKAqPxv2ef6+8L6JwhqAnAVYFiV5Zjrgam56e/UrHguDFmOlAzh4eeREI3bxpjngGmIF4pgMnheJ+euc9h18XAQ9baicaYvyEnm0uAD5EveTFSR2gekJ5DH/60qRPgBmoDlYDZxpjp1tqtDtuV+dzOQKK1drWv7CmiXaFAd6AjkAj8YYxZYq39w2G79gEx1tojxpj2wCRjzHnWWp9dpRXSrueA1621Ccbk9Ld0zC6stQuA84wxLYBPjDG/WGuTnbYr47nnAS8hV5Lnxp9nKqc3oCmwMGN/FDAqy2PTgC5+tOU43nkOBjiRy3HzgJZO2gS8AwzPctyHwN8C5bMCXgeecOD3lNvnNQz4OMtxTwOPOG1XDsfFAR2ctguYjYwDbUeuZI8C9zltVw7H/RkIn1fG/brARqBbfvsr9TF7Y0z1jNsQ4CkgMwNhCjDMGBNhjGkANAEW+tG0vUCvjP2LgE0ZdkYbY8pk7PcF0q21a520CbmUvMgIZYALgfV+sikvuzK/12uQMJO/yc2uaUDrjO8yNOMYf32HudpljKlmjHFl7DdEfvM+uzorrF3W2h7W2lhrbSwwDnjBWvu203YZYxpkfH8YY+oj43/bA8CuisBPiLM6N7+dlYowzjm4zhhzb8b+98BHANbaNcaYb5A/YTpwr/XvINrtwBsZP6ZkJFYPkoEzzRjjQeJ0wwPApneQz2014mF8ZK1dGQB2AfQEdlsfhpSKape19pgx5jUkE8wCP1trf3LaLuSz+pcxJh0Jy91lrT0aAHY5TW52dQceN8akAR7gHmutP8sf52bXfUBj4GljzNMZbZdaSejIFS2XoCiKEgSU+jCOoiiKomKvKIoSFKjYK4qiBAEq9oqiKEGAir2iKEoQoGKvKIoSBKjYK4qiBAH/D4shcPu+xzKhAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MAP = tractMAP  #committing to the trimmed map (for TN, using convex hull)\n",
    "#bufferDistance = 0.02  #do minor buffer for OH\n",
    "origMAP = wholeMAP    # let's keep a copy of the original map prior to buffering\n",
    "#MAPbuffer = MAP.exterior.buffer(bufferDistance, single_sided=True)  #gives a little exterior buffer\n",
    "# MAP = origMAP.convex_hull  #alternate to buffer for weird state shapes\n",
    "bBox = MAP.bounds\n",
    "MAPMinX = bBox[0]  #these four are useful for slicing ops, and are basis for map grid (without buffer)\n",
    "MAPMinY = bBox[1]\n",
    "MAPMaxX = bBox[2]\n",
    "MAPMaxY = bBox[3]\n",
    "\n",
    "#MAP = MAP.union(MAPbuffer)  #not buffering for TN\n",
    "print(\"original and final map areas are\",origMAP.area,MAP.area)  #same if we didn't buffer\n",
    "x2, y2 = MAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"green\")\n",
    "x2, y2 = origMAP.exterior.xy\n",
    "plt.plot(x2,y2,c=\"blue\")\n",
    "plt.show()\n",
    "\n",
    "minTractPop = 10  #was zero to not exclude empty interiors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "8e271f55-e118-4575-bc99-c28c59f435b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020 population, Trump+Biden voters, lean =  10077314 5453819 0.48586542278500044\n",
      "compare to Census 10077331.0 Leip has Trump + Biden = 5453909.0 0.4858650923585267\n"
     ]
    }
   ],
   "source": [
    "#let's confirm some population stats - these match 2020 census, USelectionatlas.org :-)\n",
    "censusPop = 10077331.\n",
    "atlasTrump = 2649864.\n",
    "atlasBiden = 2804045.\n",
    "\n",
    "print(\"2020 population, Trump+Biden voters, lean = \",np.sum(tractPop),np.sum(vtdPop),stateGOP )\n",
    "print(\"compare to Census\",censusPop,\"Leip has Trump + Biden =\",atlasTrump+atlasBiden,\n",
    "      atlasTrump/(atlasTrump+atlasBiden) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6c47f717-f208-4a4c-b825-a3181a83c6ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "state pop before, after slice operation are 10077314 10077292.0\n",
      "state Trumpers before, after slice opn are 2649829 2649829.0\n",
      "state Bideners before, after slice opn are 2803990 2803990.0\n",
      "missed pop for tract, pop, (x,y) 2403 20 -82.43924527129954 42.98693545968116\n",
      "missed pop for tract, pop, (x,y) 2742 2 -82.8271985007431 42.613782360317444\n"
     ]
    }
   ],
   "source": [
    "#Check on integrity after slicing\n",
    "sumPop = 0.\n",
    "sumTrump = 0.\n",
    "sumBiden = 0.\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] == 0:\n",
    "        sumPop += tractPop[t]\n",
    "for p in range(nPrecincts):\n",
    "    if isSkippedPrecinct[p] == 0:\n",
    "        sumTrump += vtdTrump[p]\n",
    "        sumBiden += vtdBiden[p]\n",
    "print(\"state pop before, after slice operation are\",np.sum(tractPop),sumPop)\n",
    "print(\"state Trumpers before, after slice opn are\",np.sum(vtdTrump),sumTrump)\n",
    "print(\"state Bideners before, after slice opn are\",np.sum(vtdBiden),sumBiden)\n",
    "for t in range(nTracts):\n",
    "    if isSkippedTract[t] ==1 and tractPop[t] > 0:  #should not occur\n",
    "        print(\"missed pop for tract, pop, (x,y)\",t,tractPop[t],tractGeom[t].centroid.x,\n",
    "              tractGeom[t].centroid.y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9c8e462-1ac7-46a9-bd63-feda864d41f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "G = float(nTracts)**0.5 ... #trying to figure out optimal G for number of tracts and districts.  come back to this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "2f6c72cd-90d8-489d-9b15-d4278ba17b3d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map has a total of 90 grids for 13 districts\n",
      "starting grid no 0 at x = -86.3496553, y = 41.914850944444446 \n",
      "starting grid no 5 at x = -86.3496553, y = 44.10218038888888 \n",
      "starting grid no 10 at x = -85.9337659, y = 42.352316833333326 \n",
      "starting grid no 15 at x = -85.9337659, y = 44.53964627777777 \n",
      "starting grid no 20 at x = -85.5178765, y = 42.78978272222221 \n",
      "starting grid no 25 at x = -85.5178765, y = 44.97711216666666 \n",
      "starting grid no 30 at x = -85.1019871, y = 43.22724861111111 \n",
      "starting grid no 35 at x = -85.1019871, y = 45.41457805555555 \n",
      "starting grid no 40 at x = -84.68609769999999, y = 43.66471449999999 \n",
      "starting grid no 45 at x = -84.2702083, y = 41.914850944444446 \n",
      "starting grid no 50 at x = -84.2702083, y = 44.10218038888888 \n",
      "starting grid no 55 at x = -83.8543189, y = 42.35231683333333 \n",
      "starting grid no 60 at x = -83.8543189, y = 44.53964627777777 \n",
      "starting grid no 65 at x = -83.4384295, y = 42.78978272222221 \n",
      "starting grid no 70 at x = -83.4384295, y = 44.97711216666666 \n",
      "starting grid no 75 at x = -83.02254009999999, y = 43.227248611111115 \n",
      "starting grid no 80 at x = -83.02254009999999, y = 45.41457805555555 \n",
      "starting grid no 85 at x = -82.60665069999999, y = 43.664714499999995 \n",
      "done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids = 738518.674158858 8800426.98774075 75.0\n"
     ]
    }
   ],
   "source": [
    "# this section - ESTIMATE POP'N DENSITY at scale of 1/3 of district length\n",
    "# also, IDENTIFY WHICH TRACTS and VTD's INTERSECT EACH GRID to speed later intersection searches by grid \n",
    "# (above sections already pulled in Tract and Precinct geometry and demographic data, built an overall map)\n",
    "nDistricts = 13   #MI congressional\n",
    "avgDistrictPop = np.sum(tractPop) / float(nDistricts)\n",
    "#MAPMaxX = -80.   #In most state maps, we figure this out from the bounding box of the convex hull; see above.\n",
    "#MAPMaxY = 31.1   # ... but for Florida, we will do manually\n",
    "#MAPMinX = -87.0\n",
    "#MAPMinY = 25.5\n",
    "stateWidth = float(MAPMaxX - MAPMinX)\n",
    "stateHeight = float(MAPMaxY - MAPMinY)\n",
    "stateWHRatio = stateWidth / stateHeight\n",
    "G = 2.5  #adjustable parameter; G*G = number of grids that fit in an average district\n",
    "nGridsX = int( G*round((nDistricts*stateWHRatio)**0.5,0) )   #OK to have empty grids for a non-rectglr state\n",
    "nGridsY = int(round(nGridsX / stateWHRatio,0) )  #so grids are square even if state has high aspect ratio\n",
    "nGrids = int(nGridsX*nGridsY)\n",
    "print(\"map has a total of {0} grids for {1} districts\".format(nGrids,nDistricts) )\n",
    "nGridPrecincts = [0]*nGrids # will store how many precincts intersect with each grid square\n",
    "nGridTracts = [0]*nGrids    # same, but for tracts\n",
    "gridPrecinctNo = [[0]*nPrecincts for gridNo in range(nGrids) ] # initialize a list of VTDs that intersect with each grid square\n",
    "gridTractNo = [[0]*nTracts for gridNo in range(nGrids) ] # initialize a list of tracts that intersect with each grid square\n",
    "\n",
    "gridPop = [0.]*nGrids #will store approx total population for this grid square\n",
    "gridDensity = [0.]*nGrids\n",
    "gridGeom = [Polygon([(0,0),(0,1),(1,0)])]*nGrids  #initialize grid geometry\n",
    "gridWidth = stateWidth /nGridsX        #geo length of a grid's side length\n",
    "gridHeight = stateHeight /nGridsY    \n",
    "\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG/nGridsY)\n",
    "    y = int(nG % nGridsY)\n",
    "    point1 = Point(x*gridWidth+MAPMinX,y*gridHeight+MAPMinY)\n",
    "    point2 = Point((x+1)*gridWidth+MAPMinX, y*gridHeight+MAPMinY)\n",
    "    point3 = Point((x+1)*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    point4 = Point(x*gridWidth+MAPMinX, (y+1)*gridHeight+MAPMinY)\n",
    "    gridGeom[nG] = Polygon([point1, point2, point3, point4])\n",
    "    if (nG %5 == 0): #print occasionally, should take about one second per grid\n",
    "        print(\"starting grid no {0} at x = {1}, y = {2} \".format(nG,gridGeom[nG].centroid.x,gridGeom[nG].centroid.y) )\n",
    "    counter = 0\n",
    "    #    Now for each grid square, find intersxn with all polygons, total the intersection population\n",
    "    #    Also, create 2 lists: of TRACTS and precincts that intersect each grid (efficiency shortcut)\n",
    "    \n",
    "    if( gridGeom[nG].intersection(MAP).area > 0.0001) : #don't bother w grids off the map\n",
    "        for t in range(nTracts):\n",
    "            # if (t%3000 == 0) :\n",
    "            #    print (\"grid {0}, tract no {1}, ({2},{3})\".format(nG,t,tractGeom[t].centroid.x,tractGeom[t].centroid.y) )\n",
    "            intersxnArea = 0.\n",
    "            if tractGeom[t].type == clipPoly.type :  # tract is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(tractGeom[t]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #tract is a multiPolygon, do the polygon intersections individually\n",
    "                #print(\"I think tract\",t,\"is a multiPolygon\")\n",
    "                for geom in tractGeom[t].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            if (intersxnArea > 0 and tractPop[t] > minTractPop) : # this tract is at least partially in this grid and populated \n",
    "                if (tractArea[t] == 0):  #should not happen, but debugging\n",
    "                    print(t,tractGeom[t].centroid.x, tractGeom[t].centroid.y)  #debug print\n",
    "                gridPop[nG] += tractPop[t]*intersxnArea/tractArea[t]\n",
    "                if tractPop[t] > (1.0 * avgDistrictPop) :  #flag if we have a mega tract\n",
    "                    uhoh = input(\"Uh oh! We have a tract that's bigger than a district.  What now?\")\n",
    "                else :\n",
    "                    gridTractNo[nG][counter] = t  #add this tract to this grid's list, update the no of tracts in grid\n",
    "                    counter +=1            \n",
    "                    nGridTracts[nG] = counter\n",
    "        # OK, now, loop over PRECINCTS to develop each grid's list of precincts (prev loop was for tracts)\n",
    "        counter = 0\n",
    "        for p in range(nPrecincts):\n",
    "            intersxnArea = 0.\n",
    "            if vtdGeom[p].type == clipPoly.type :  # precinct is a simple polygon\n",
    "                intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area  #replaced tractGeom w cleanedPoly\n",
    "            else : #precinct is a multiPolygon, do the polygon intersections individually\n",
    "                for geom in vtdGeom[p].geoms :\n",
    "                    intersxnArea += gridGeom[nG].intersection(geom).area\n",
    "            intersxnArea = gridGeom[nG].intersection(vtdGeom[p]).area\n",
    "            if (intersxnArea > 0 and vtdPop[p] >0) : # this precinct is at least partially in this grid and recorded votes                 \n",
    "                gridPrecinctNo[nG][counter] = p  #add this precinct to this grid's list, update the no of tracts in grid\n",
    "                counter +=1            \n",
    "                nGridPrecincts[nG] = counter\n",
    "    gridDensity[nG] = gridPop[nG] / gridGeom[nG].area  #finished loop over tracts & vtd's.  Calceach grid's population density    \n",
    "\n",
    "minGridPop = np.min(gridPop)\n",
    "# avgGridPop = np.mean(gridPop)   #see below for explicit averaging, since there are so many empty grids\n",
    "maxGridPop = np.max(gridPop)\n",
    "nPopulatedGrids = 0.\n",
    "totPopulatedGridArea = 0.\n",
    "for nG in range(nGrids) :\n",
    "    if gridPop[nG] > 0. :\n",
    "        nPopulatedGrids +=1\n",
    "        totPopulatedGridArea += gridGeom[nG].area\n",
    "avgGridPop = np.sum(tractPop) / nPopulatedGrids\n",
    "avgGridDensity = np.sum(tractPop) / totPopulatedGridArea\n",
    "#avgGridDensity = np.average(gridDensity) #avgGridPop / (gridPL * gridPL) #these densities help home district algorithm\n",
    "maxGridDensity = np.max(gridDensity)\n",
    "print(\"done building grids. avgGridDensity, maxGridDensity,nPopulatedGrids =\",avgGridDensity,maxGridDensity,nPopulatedGrids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "808f513f-21af-4b03-8061-fdeeed33dafd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a heat map of grid density \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(close=None, block=None)>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"here is a heat map of grid density \")\n",
    "xyGridDensity = [[0.]*nGridsX for x in range (nGridsY) ]   #flipping x and y to get right orientation in pixel grid\n",
    "for nG in range (nGrids) : #  create polygon shape for each grid square, compute grid-square population density\n",
    "    x = int(nG % nGridsY)\n",
    "    y = int(nG / nGridsY)\n",
    "    if gridDensity[nG] > 0 :  #avoid log zero\n",
    "        xyGridDensity[x][y]=np.log(gridDensity[nG])\n",
    "c=plt.pcolormesh(xyGridDensity)\n",
    "plt.colorbar(c)\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "4013f80b-4fc8-40a1-b708-ee29c29b3ffe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.3940077030921224e-06 9.730771500007868e-06\n",
      "10 13 MI\n"
     ]
    }
   ],
   "source": [
    "print(np.min(vtdArea), np.min(tractArea))\n",
    "print(minTractPop,nDistricts,STATE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "ab450500-f1a4-4292-a2fe-71eb2217121d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 0 of 3017 tracts\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_235/99954720.py:66: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
      "/tmp/ipykernel_235/99954720.py:99: RuntimeWarning: invalid value encountered in multiply\n",
      "  wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am working on tract number 20 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 39 5.0 2 90.0 0.8294 207102.7\n",
      "I am working on tract number 40 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 43\n",
      "I am working on tract number 60 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 79\n",
      "I am working on tract number 80 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 81 4.0 0 90.0 1.8383 211010.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 81 5.0 0 90.0 1.7234 211010.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 87 8.0 3 78.5 2.8107 250879.6\n",
      "we have 2 non-opposing shorted wedges for tract no 98\n",
      "I am working on tract number 100 of 3017 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 103 0 363558.53249463637 1.6693\n",
      "8 yoyos for tract,wedge,wedgePop,r= 103 0 34788.22582095297 0.8347\n",
      "9 yoyos for tract,wedge,wedgePop,r= 103 0 362918.05255206424 1.663\n",
      "9 yoyos for tract,wedge,wedgePop,r= 103 0 201624.4341493831 1.2488\n",
      "10 yoyos for tract,wedge,wedgePop,r= 103 0 57696.20621953477 1.0417\n",
      "10 yoyos for tract,wedge,wedgePop,r= 103 0 100605.42160969801 1.1453\n",
      "10 yoyos for tract,wedge,wedgePop,r= 103 0 143351.5915034127 1.197\n",
      "10 yoyos for tract,wedge,wedgePop,r= 103 0 170125.84637506527 1.2229\n",
      "10 yoyos for tract,wedge,wedgePop,r= 103 0 185405.18714312927 1.2359\n",
      "we have 2 non-opposing shorted wedges for tract no 105\n",
      "I am working on tract number 120 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 130 3.0 1 37.5 0.9061 250723.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 130 4.0 1 37.5 0.8887 250723.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 131 5.0 1 41.1 1.0188 335069.6\n",
      "we have 2 non-opposing shorted wedges for tract no 135\n",
      "I am working on tract number 140 of 3017 tracts\n",
      "I am working on tract number 160 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 167\n",
      "I am working on tract number 180 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 197\n",
      "I am working on tract number 200 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 202\n",
      "I am working on tract number 220 of 3017 tracts\n",
      "I am working on tract number 240 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 241 8.0 0 117.7 0.8739 211654.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 241 13.0 0 117.7 0.8568 211654.0\n",
      "7 yoyos for tract,wedge,wedgePop,r= 241 0 211653.98101555204 0.9011\n",
      "I am working on tract number 260 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 270\n",
      "I am working on tract number 280 of 3017 tracts\n",
      "I am working on tract number 300 of 3017 tracts\n",
      "I am working on tract number 320 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 331\n",
      "I am working on tract number 340 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 343\n",
      "we have 2 non-opposing shorted wedges for tract no 344\n",
      "I am working on tract number 360 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 378\n",
      "I am working on tract number 380 of 3017 tracts\n",
      "I am working on tract number 400 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 404 4.0 1 36.1 1.5209 344271.1\n",
      "I am working on tract number 420 of 3017 tracts\n",
      "I am working on tract number 440 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 451\n",
      "I am working on tract number 460 of 3017 tracts\n",
      "I am working on tract number 480 of 3017 tracts\n",
      "I am working on tract number 500 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 504\n",
      "we have 2 non-opposing shorted wedges for tract no 506\n",
      "we have 2 non-opposing shorted wedges for tract no 508\n",
      "I am working on tract number 520 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 525\n",
      "we have 2 non-opposing shorted wedges for tract no 526\n",
      "we have 2 non-opposing shorted wedges for tract no 527\n",
      "I am working on tract number 540 of 3017 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 547 2 240419.63210006247 1.1383\n",
      "8 yoyos for tract,wedge,wedgePop,r= 547 2 9398.220678609243 0.5691\n",
      "9 yoyos for tract,wedge,wedgePop,r= 547 2 396455.8911967183 1.2639\n",
      "10 yoyos for tract,wedge,wedgePop,r= 547 2 46873.99295683281 0.9165\n",
      "10 yoyos for tract,wedge,wedgePop,r= 547 2 154612.79578108402 1.0902\n",
      "11 yoyos for tract,wedge,wedgePop,r= 547 2 305753.5653352253 1.177\n",
      "11 yoyos for tract,wedge,wedgePop,r= 547 2 231003.02622334863 1.1336\n",
      "I am working on tract number 560 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 564\n",
      "we have 2 non-opposing shorted wedges for tract no 565\n",
      "we have 2 non-opposing shorted wedges for tract no 566\n",
      "I am working on tract number 580 of 3017 tracts\n",
      "I am working on tract number 600 of 3017 tracts\n",
      "I am working on tract number 620 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 636 6.0 0 83.3 0.9217 202831.3\n",
      "I am working on tract number 640 of 3017 tracts\n",
      "I am working on tract number 660 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 679 7.0 0 93.9 0.505 200162.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 679 8.0 0 93.9 0.4929 200162.5\n",
      "I am working on tract number 680 of 3017 tracts\n",
      "I am working on tract number 700 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 710\n",
      "I am working on tract number 720 of 3017 tracts\n",
      "I am working on tract number 740 of 3017 tracts\n",
      "I am working on tract number 760 of 3017 tracts\n",
      "I am working on tract number 780 of 3017 tracts\n",
      "I am working on tract number 800 of 3017 tracts\n",
      "I am working on tract number 820 of 3017 tracts\n",
      "I am working on tract number 840 of 3017 tracts\n",
      "I am working on tract number 860 of 3017 tracts\n",
      "I am working on tract number 880 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 899 5.0 1 90.0 1.0316 231519.3\n",
      "I am working on tract number 900 of 3017 tracts\n",
      "I am working on tract number 920 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 930\n",
      "I am working on tract number 940 of 3017 tracts\n",
      "I am working on tract number 960 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 976 5.0 2 90.0 0.3513 174471.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 976 6.0 2 90.0 0.3797 174471.5\n",
      "I am working on tract number 980 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 986 2.0 3 90.0 0.876 213467.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 986 3.0 3 90.0 0.814 213467.8\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 986 4.0 3 90.0 0.7563 213467.8\n",
      "I am working on tract number 1000 of 3017 tracts\n",
      "I am working on tract number 1020 of 3017 tracts\n",
      "I am working on tract number 1040 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1042\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1044 7.0 0 93.2 1.0249 215035.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1044 8.0 0 93.2 0.947 215035.1\n",
      "I am working on tract number 1060 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1078\n",
      "I am working on tract number 1080 of 3017 tracts\n",
      "I am working on tract number 1100 of 3017 tracts\n",
      "I am working on tract number 1120 of 3017 tracts\n",
      "I am working on tract number 1140 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1146\n",
      "we have 2 non-opposing shorted wedges for tract no 1149\n",
      "I am working on tract number 1160 of 3017 tracts\n",
      "I am working on tract number 1180 of 3017 tracts\n",
      "I am working on tract number 1200 of 3017 tracts\n",
      "I am working on tract number 1220 of 3017 tracts\n",
      "I am working on tract number 1240 of 3017 tracts\n",
      "I am working on tract number 1260 of 3017 tracts\n",
      "I am working on tract number 1280 of 3017 tracts\n",
      "I am working on tract number 1300 of 3017 tracts\n",
      "I am working on tract number 1320 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1323\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1338 9.0 0 96.0 0.8972 207813.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1338 10.0 0 96.0 0.851 207813.7\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1338 11.0 0 96.0 0.8072 207813.7\n",
      "I am working on tract number 1340 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1347 3.0 0 109.4 0.4771 281128.0\n",
      "we have 2 non-opposing shorted wedges for tract no 1350\n",
      "I am working on tract number 1360 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1363\n",
      "I am working on tract number 1380 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1391\n",
      "we have 2 non-opposing shorted wedges for tract no 1395\n",
      "we have 2 non-opposing shorted wedges for tract no 1397\n",
      "we have 2 non-opposing shorted wedges for tract no 1399\n",
      "I am working on tract number 1400 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1400\n",
      "we have 2 non-opposing shorted wedges for tract no 1401\n",
      "I am working on tract number 1420 of 3017 tracts\n",
      "I am working on tract number 1440 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1451 4.0 0 83.4 0.8238 253457.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1455 2.0 0 90.0 0.6728 215636.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1455 3.0 0 90.0 0.6203 215636.2\n",
      "we have 2 non-opposing shorted wedges for tract no 1458\n",
      "I am working on tract number 1460 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1477 6.0 0 83.3 2.5783 235678.4\n",
      "I am working on tract number 1480 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1494\n",
      "we have 2 non-opposing shorted wedges for tract no 1496\n",
      "we have 2 non-opposing shorted wedges for tract no 1497\n",
      "we have 2 non-opposing shorted wedges for tract no 1498\n",
      "I am working on tract number 1500 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1504\n",
      "I am working on tract number 1520 of 3017 tracts\n",
      "I am working on tract number 1540 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1540\n",
      "I am working on tract number 1560 of 3017 tracts\n",
      "I am working on tract number 1580 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1585 3.0 3 63.1 0.9126 232354.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1585 4.0 3 63.1 0.9037 232354.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1585 5.0 3 63.1 0.8948 232354.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1585 6.0 3 63.1 0.8861 232354.9\n",
      "I am working on tract number 1600 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1607 3.0 1 90.0 0.9385 194641.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1608 3.0 1 90.0 1.0144 203159.6\n",
      "we have 2 non-opposing shorted wedges for tract no 1617\n",
      "we have 2 non-opposing shorted wedges for tract no 1619\n",
      "I am working on tract number 1620 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1620\n",
      "we have 2 non-opposing shorted wedges for tract no 1635\n",
      "I am working on tract number 1640 of 3017 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1659 0 459937.177490065 1.7025\n",
      "8 yoyos for tract,wedge,wedgePop,r= 1659 0 38849.141780909675 0.8512\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1659 0 459912.67743749305 1.7022\n",
      "9 yoyos for tract,wedge,wedgePop,r= 1659 0 268578.680081144 1.2767\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1659 0 75099.42042622232 1.064\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1659 0 135919.46255656527 1.1704\n",
      "10 yoyos for tract,wedge,wedgePop,r= 1659 0 185100.721333128 1.2236\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1659 0 225394.8964049082 1.2501\n",
      "11 yoyos for tract,wedge,wedgePop,r= 1659 0 204273.23338005372 1.2368\n",
      "I am working on tract number 1660 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1668\n",
      "7 yoyos for tract,wedge,wedgePop,r= 1671 3 533819.3637355657 2.6638\n",
      "we have 2 non-opposing shorted wedges for tract no 1674\n",
      "I am working on tract number 1680 of 3017 tracts\n",
      "I am working on tract number 1700 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1717\n",
      "I am working on tract number 1720 of 3017 tracts\n",
      "I am working on tract number 1740 of 3017 tracts\n",
      "I am working on tract number 1760 of 3017 tracts\n",
      "I am working on tract number 1780 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1794\n",
      "we have 2 non-opposing shorted wedges for tract no 1796\n",
      "I am working on tract number 1800 of 3017 tracts\n",
      "I am working on tract number 1820 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1824\n",
      "we have 2 non-opposing shorted wedges for tract no 1825\n",
      "we have 2 non-opposing shorted wedges for tract no 1826\n",
      "we have 2 non-opposing shorted wedges for tract no 1828\n",
      "we have 2 non-opposing shorted wedges for tract no 1829\n",
      "we have 2 non-opposing shorted wedges for tract no 1831\n",
      "I am working on tract number 1840 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1841 2.0 3 90.0 0.7778 226746.3\n",
      "we have 2 non-opposing shorted wedges for tract no 1845\n",
      "we have 2 non-opposing shorted wedges for tract no 1854\n",
      "we have 2 non-opposing shorted wedges for tract no 1855\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 1856 5.0 3 83.9 0.6634 221803.8\n",
      "I am working on tract number 1860 of 3017 tracts\n",
      "I am working on tract number 1880 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1883\n",
      "we have 2 non-opposing shorted wedges for tract no 1884\n",
      "I am working on tract number 1900 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1918\n",
      "I am working on tract number 1920 of 3017 tracts\n",
      "I am working on tract number 1940 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 1941\n",
      "we have 2 non-opposing shorted wedges for tract no 1955\n",
      "I am working on tract number 1960 of 3017 tracts\n",
      "I am working on tract number 1980 of 3017 tracts\n",
      "I am working on tract number 2000 of 3017 tracts\n",
      "I am working on tract number 2020 of 3017 tracts\n",
      "I am working on tract number 2040 of 3017 tracts\n",
      "I am working on tract number 2060 of 3017 tracts\n",
      "I am working on tract number 2080 of 3017 tracts\n",
      "I am working on tract number 2100 of 3017 tracts\n",
      "I am working on tract number 2120 of 3017 tracts\n",
      "I am working on tract number 2140 of 3017 tracts\n",
      "I am working on tract number 2160 of 3017 tracts\n",
      "I am working on tract number 2180 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2199\n",
      "I am working on tract number 2200 of 3017 tracts\n",
      "I am working on tract number 2220 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 2.0 1 90.0 0.907 195691.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 3.0 1 90.0 0.9004 195691.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 4.0 1 90.0 0.8938 195691.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 5.0 1 90.0 0.8873 195691.5\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2233 6.0 1 90.0 0.8809 195691.5\n",
      "I am working on tract number 2240 of 3017 tracts\n",
      "I am working on tract number 2260 of 3017 tracts\n",
      "7 yoyos for tract,wedge,wedgePop,r= 2271 3 269990.0891878812 1.9347\n",
      "I am working on tract number 2280 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2290\n",
      "we have 2 non-opposing shorted wedges for tract no 2293\n",
      "I am working on tract number 2300 of 3017 tracts\n",
      "I am working on tract number 2320 of 3017 tracts\n",
      "I am working on tract number 2340 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2347\n",
      "I am working on tract number 2360 of 3017 tracts\n",
      "I am working on tract number 2380 of 3017 tracts\n",
      "I am working on tract number 2400 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2401\n",
      "we have 2 non-opposing shorted wedges for tract no 2404\n",
      "we have 2 non-opposing shorted wedges for tract no 2405\n",
      "we have 2 non-opposing shorted wedges for tract no 2407\n",
      "we have 2 non-opposing shorted wedges for tract no 2414\n",
      "we have 2 non-opposing shorted wedges for tract no 2415\n",
      "we have 2 non-opposing shorted wedges for tract no 2416\n",
      "I am working on tract number 2420 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2427\n",
      "I am working on tract number 2440 of 3017 tracts\n",
      "I am working on tract number 2460 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2471 2.0 2 90.0 0.6361 498570.1\n",
      "I am working on tract number 2480 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2480 5.0 0 90.0 0.6797 204919.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2480 6.0 0 90.0 0.6516 204919.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2480 11.0 0 90.0 0.6826 204919.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2480 12.0 0 90.0 0.6545 204919.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2480 13.0 0 90.0 0.6274 204919.1\n",
      "we have 2 non-opposing shorted wedges for tract no 2493\n",
      "we have 2 non-opposing shorted wedges for tract no 2494\n",
      "I am working on tract number 2500 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2518\n",
      "I am working on tract number 2520 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2521 3.0 0 86.3 0.8749 217641.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2521 4.0 0 86.3 0.8009 217641.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2521 5.0 0 86.3 0.7332 217641.3\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2529 5.0 0 83.3 0.8322 247252.4\n",
      "I am working on tract number 2540 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2546 2.0 2 90.0 0.8897 208606.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2546 3.0 2 90.0 0.8415 208606.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2546 4.0 2 90.0 0.7959 208606.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2546 5.0 2 90.0 0.7527 208606.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2546 6.0 2 90.0 0.7119 208606.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2546 7.0 2 90.0 0.6733 208606.0\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2546 8.0 2 90.0 0.6368 208606.0\n",
      "I am working on tract number 2560 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2574\n",
      "we have 2 non-opposing shorted wedges for tract no 2577\n",
      "I am working on tract number 2580 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2584\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2591 5.0 0 84.5 1.5963 216709.4\n",
      "I am working on tract number 2600 of 3017 tracts\n",
      "I am working on tract number 2620 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2628 6.0 1 68.0 1.6783 251689.6\n",
      "I am working on tract number 2640 of 3017 tracts\n",
      "loop31.0, tr2650,wedgePops50614.07, 1659.4, 22209.9, 4.4, 26740.4, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop32.0, tr2650,wedgePops52059.76, 1659.4, 22998.4, 4.4, 27397.6, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop33.0, tr2650,wedgePops53498.85, 1659.4, 23777.9, 4.4, 28057.2, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop34.0, tr2650,wedgePops54928.97, 1659.4, 24547.3, 4.4, 28717.9, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop35.0, tr2650,wedgePops56350.84, 1659.4, 25306.1, 4.4, 29380.9, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop36.0, tr2650,wedgePops57777.73, 1659.4, 26061.9, 4.4, 30052.0, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop37.0, tr2650,wedgePops59215.22, 1659.4, 26821.0, 4.4, 30730.4, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop38.0, tr2650,wedgePops60660.79, 1659.4, 27585.3, 4.4, 31411.6, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "loop39.0, tr2650,wedgePops62131.79, 1659.4, 28369.9, 4.4, 32098.1, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.73562 1152.2073 1659.4345 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 33.63066 27585.3331 28369.8762 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.91826 0.0569 4.3952 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 33.63066 31411.6315 32098.0827 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loop40.0, tr2650,wedgePops63653.8, 1659.4, 29199.2, 4.4, 32790.8, Overedge?1, 0, 1, 0, ,Satisfied?0000,yoyo?0000 \n",
      "   targetWP, latest drx4 are tWP,dr, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087, 386757.1,0.9087\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 0 2.73562 1152.2073 1659.4345 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 1 34.53934 28369.8762 29199.2157 0\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 2 0.91826 0.0569 4.3952 1\n",
      "wedge no, currentR,oldWedgePop, wedgePop, overEdge? 3 34.53934 32098.0827 32790.7587 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I looped 40 times on tract 2650, giving up w pop 63653.80410800157\n",
      "I am working on tract number 2660 of 3017 tracts\n",
      "I am working on tract number 2680 of 3017 tracts\n",
      "I am working on tract number 2700 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 3.0 0 123.6 0.8737 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 4.0 0 123.6 0.8076 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 5.0 0 123.6 0.7466 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 6.0 0 123.6 0.6902 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 7.0 0 123.6 0.638 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 8.0 0 123.6 0.5898 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 9.0 0 123.6 0.5452 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 10.0 0 123.6 0.504 214904.1\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2708 11.0 0 123.6 0.4659 214904.1\n",
      "we have 2 non-opposing shorted wedges for tract no 2711\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2717 4.0 3 90.0 1.6108 215654.4\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2717 5.0 3 90.0 1.485 215654.4\n",
      "I am working on tract number 2720 of 3017 tracts\n",
      "I am working on tract number 2740 of 3017 tracts\n",
      "I am working on tract number 2760 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2765 9.0 0 125.7 1.0171 197350.5\n",
      "I am working on tract number 2780 of 3017 tracts\n",
      "we have 2 non-opposing shorted wedges for tract no 2793\n",
      "I am working on tract number 2800 of 3017 tracts\n",
      "I am working on tract number 2820 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2822 10.0 0 99.6 0.8041 200625.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2822 11.0 0 99.6 0.7833 200625.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2822 12.0 0 99.6 0.7632 200625.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2822 13.0 0 99.6 0.7435 200625.6\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2832 8.0 0 119.3 1.022 224231.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2832 9.0 0 119.3 0.9536 224231.2\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2832 10.0 0 119.3 0.8899 224231.2\n",
      "I am working on tract number 2840 of 3017 tracts\n",
      "I am working on tract number 2860 of 3017 tracts\n",
      "I am working on tract number 2880 of 3017 tracts\n",
      "I am working on tract number 2900 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 2900 3.0 0 88.9 0.4429 367592.9\n",
      "I am working on tract number 2920 of 3017 tracts\n",
      "I am working on tract number 2940 of 3017 tracts\n",
      "I am working on tract number 2960 of 3017 tracts\n",
      "I am working on tract number 2980 of 3017 tracts\n",
      "I am working on tract number 3000 of 3017 tracts\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3000 11.0 0 86.9 0.6555 202677.9\n",
      "we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop 3000 12.0 0 86.9 0.6337 202677.9\n",
      "Sum of weights over all precinct home districts should have been 1.000, but was  1.000327175953632\n",
      "Average and max number of wedgePop loops per tract were:  5.657275439177991 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.7049047700215471 4.104812282753314\n",
      "calculated statewide vote was 0.48587138644077016, should have been 0.48586542278500044\n",
      "calcd statewide Hispanic pop was 0.0441898517336008, should have been 0.04644870329550368\n",
      "calcd statewide Black pop was 0.12355242227145435, should have been 0.13028134579603623\n",
      "fraction of HDs that with altered wedge angles near boundaries =  0.308584686774942\n"
     ]
    }
   ],
   "source": [
    "#3/13/22 - from HD2, but using coeff1, coeff2 (unrestricted opp wedge pop, wideAngle quadratic in ratio)\n",
    "# Bisect for yoyo > 3, precinct calcs after wedges finalized.  More aggressive Euler gain\n",
    "#minTractPop = 10  #this is set in a prior block\n",
    "pi=3.1415926536\n",
    "nWedges = 4  #number of wedges per home district polygon  \n",
    "avgWedgeAngle = 2*pi / nWedges\n",
    "wedgeTriAR = [math.sin(pi/nWedges)*math.cos(pi/nWedges)]*nWedges  #wedge triangle area ratio (= area / r*r)\n",
    "angle = [0.]*nWedges\n",
    "angle2 = [0.]*nWedges\n",
    "pt1 = [Point(0,0)]*nWedges  #these four define the polygon wedge for a growing home district\n",
    "pt2 = [Point(0,0)]*nWedges\n",
    "pt3 = [Point(0,0)]*nWedges\n",
    "pt4 = [Point(0,0)]*nWedges\n",
    "oldR = [0.]*nWedges  #wedge radius from previous loop\n",
    "# guessedR = [0.]*nWedges  #obsolete; was wedge radius if we extrapolate from population density of most recent wedge piece\n",
    "printPoly = [Polygon([(0,0),(0,1),(1,1)])]*nWedges #for debugging\n",
    "wedgePop = [0.]*nWedges\n",
    "tractLoopCounter = [0.]* nTracts  #this tracks how many loops per tract to get to target wedge Pop\n",
    "nearEdge = [0.]* nTracts   #not yet implemented; this will flag tracts near map edge for reprocessing\n",
    "tractUse = [0.] * nTracts  #this will store how much we use this tract in ALL HD's vs. expectation\n",
    "precinctUse = [0.] * nPrecincts   #same, but for each precinct / VTD\n",
    "loopTractUse = [0.] * nTracts  #same as above two, but only for the current loop / tract\n",
    "loopPrecinctUse = [0.] * nPrecincts\n",
    "HDvPop = [0.]*nTracts\n",
    "HDvGOP = [0.]*nTracts  #GOP lean of each tract's \"home district\"\n",
    "HDvBlack = [0.]*nTracts\n",
    "HDvHisp = [0.]*nTracts #pct Hispanic by home district\n",
    "HDweight = [0.]*nTracts  #relative weight of each district.  In absence of splits, will equal precinct pop\n",
    "HDarea = [0.]*nTracts  #geographical area of each home district\n",
    "HDradius = [[0.]*nWedges for t in range(nTracts)] #final wedge length for each Home District wedge\n",
    "HDangle = [[0.]*nWedges for t in range(nTracts)] #final included angle for each HD wedge\n",
    "angle0 = [-999.] * nTracts  #orientation of 0th wedge.  Random except re-oriented if we are near-boundary\n",
    "toler = 0.005  #adjustable - fractional slop in district population.  normally 0.01, to be reduced to 0.005\n",
    "tolerPop = toler * avgDistrictPop  #absolute slop in district pop\n",
    "nLoopPrint = 30  #at this loop number, we alert user of problem\n",
    "nGiveUp = 40  #we punt after this many loops\n",
    "nLoopSuperPrint = nGiveUp - 2 # at this loop number, we output wedge growth visuals\n",
    "wrongPop = [0.]*nTracts\n",
    "normalGain = 0.8  #adjustable - a bit less than 1.0 for stability, how far we step relative to expected perfect guess\n",
    "EulerGain = 1.5 #if we hit empty land, gain more aggressively to get through it\n",
    "#yoyoFactor = 0.3 #rate of reduction in gain as solver continues to yoyo in solving a wedge  #not currently used ***\n",
    "globalMax_dr = (MAP.area/nDistricts) ** 0.5  #put a reasonable upper limit on wedge radial growth step size\n",
    "tractPrintInterval = 20  #for tracking progress\n",
    "minTractArea = 0.00001 * MAP.area / nTracts  #failsafe for later div-by-zero\n",
    "homePopDensity = avgGridDensity  #seed this\n",
    "didWeRestart = [0] * nTracts  #will flag if we adjusted wedge angles due to a single shorted wedge\n",
    "coeff1 = 0.3  #linear term. we will adjust this empirically to tighten tract weighting near boundaries\n",
    "coeff2 = 0.3  #quadratic term.  These two control distortion in wedge angles relative to wedgePop shortness\n",
    "minWedgePop = [avgDistrictPop/nWedges] * nTracts  #wedge pop for wedge facing boundary (for tracts near boundaries)\n",
    "maxWideAngle = 0.8 * pi  #adjustable parameter.  (avoid wedges too close to half a pie\n",
    "\n",
    "for t in range(nTracts) :  #(nTracts):  #loop on each tract.  Start by resetting stats\n",
    "    gain = normalGain  #in case last precinct had convergence problems\n",
    "    if (t % tractPrintInterval) == 0 : \n",
    "        print(\"I am working on tract number {0} of {1} tracts\".format(t,nTracts) )\n",
    "    tractLoopCounter[t] = 0.  #for stability stats, will track how many times we need to loop for each tract\n",
    "    # isActiveG = [0]*nGrids  #resets whether each grid is relevant to this tract (not used; see alt grid turn-on method)\n",
    "    if (tractArea[t] > minTractArea) :  #too lazy to indent everything.  We'll catch zero tractArea later\n",
    "        homePopDensity = tractPop[t]/tractArea[t]  #temporary - estimate the local density for initial step\n",
    "    tractCP = Point(tractGeom[t].centroid.x,tractGeom[t].centroid.y)  #shorthand for centroid of each tract\n",
    "    for nG in range(nGrids):\n",
    "        if gridGeom[nG].contains(tractCP) :  #which grid contains the centroid of this tract?  Turn it on!\n",
    "            # isActiveG[nG] = 1  #I don't think I use this\n",
    "            homeGridDensity = gridDensity[nG]\n",
    "    maxStartDensity = max(homePopDensity,homeGridDensity)\n",
    "    minR = (avgDistrictPop / maxStartDensity / pi)**0.5   # conservative estimated radius of this tract's district\n",
    "    tinyR = 0.01 * minR   #ensures we start with small, but not infinitesimal wedge populations\n",
    "    angle0[t] = random.uniform(0,2.*pi)  #imparts random orientation to our starting wedge\n",
    "    loopTractUse = [0.]*nTracts  #this and below help us reset tract n precinct use after a wedge reset\n",
    "    loopPrecinctUse = [0.]*nPrecincts\n",
    "    # ***GROW WEDGES SIMULTANEOUSLY via ARRAYS, SO WE CAN REACT ON FLY TO BOUNDARY STOPS\n",
    "    HDpop = 0.  #running total of this tract's \"home district\" population\n",
    "    nActiveWedges = nWedges #active wedges have not run over boudnary\n",
    "    targetWedgePop = [avgDistrictPop / nWedges]*nWedges  #at beginning, split districtPop equally among wedges\n",
    "    #latestWedgeDensity = [0.]*nWedges  #stop using # pop'n density of the wedge piece we just added or subtracted\n",
    "    wedgePop = [0.]*nWedges\n",
    "    oldWedgePop = [0.]*nWedges  #wedge pop from previous round (needed for NR projection)\n",
    "    wedgePopGap = [0.]*nWedges\n",
    "    isOverEdge = [0] *nWedges #each wedge is still fully inside map boundary\n",
    "    isSatisfied = [0] * nWedges #wedges close enough to target are not improved upon\n",
    "    yoyoCount = [0] * nWedges #tracks how many times we've reversed this wedge's growth vs. shrink at current wPop target\n",
    "    wedgeMaxR = [0.] * nWedges #will track the largest radius this wedge has seen (to cap yoyo bisection step)\n",
    "    wedgeAngle = [avgWedgeAngle] * nWedges #reset to equi-angle when starting each tract    \n",
    "    currentR = [0.]*nWedges\n",
    "    old_dr = [0.]*nWedges\n",
    "    wedgeStop = 0  #obsolete? this will flag if we need to rejigger oldR when we stopped an over-boundary wedge\n",
    "    for nW in range(nWedges):\n",
    "        currentR[nW] = tinyR  #each wedge starts as a tiny triangle of this radius\n",
    "        old_dr[nW] = currentR[nW] #this will track the last radial step (to help convergence)\n",
    "    oldR = [0.]*nWedges   #for remembering last loop's wedge radius\n",
    "    max_dr = [globalMax_dr]*nWedges  #reset max possible positive radial step size\n",
    "    for nW in range(nWedges) :  #this loop: set up tiny wedges in each direction to seed each wedge loop\n",
    "        angle[nW] = (nW-0.5)*wedgeAngle[nW]+angle0[t]   #local variable for orientation of START of wedge\n",
    "        angle2[nW] = angle[nW]+wedgeAngle[nW]      #local angle for clockwise END of wedge\n",
    "        pt1[nW] = tractCP\n",
    "        pt2[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle[nW]),  tractCP.y+currentR[nW]*math.sin(angle[nW]) )\n",
    "        pt3[nW] = Point(tractCP.x+currentR[nW]*math.cos(angle2[nW]), tractCP.y+currentR[nW]*math.sin(angle2[nW]) )\n",
    "        wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ]) #build a tiny starter wedge triangle\n",
    "        wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)  #max prevents rare div-by-zero\n",
    "    HDpop = np.sum(wedgePop)\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1):\n",
    "        #go directly to jail, do not pass Go, this tract doesn't count\n",
    "        HDpop = avgDistrictPop  #white lie to kick us out of loop\n",
    "    while abs (HDpop - avgDistrictPop) > tolerPop :  #until we've grown the home district to the right size...\n",
    "        sumWedgePopGapChange = 0  #this will total wedge pops that fall short of expectations (when over boundary)\n",
    "        for nW in range(nWedges) :  #for each wedge, we'll build to gain pop or shrink to lose population ...\n",
    "            neededWedgePop = targetWedgePop[nW] - wedgePop[nW]\n",
    "            isSatisfied[nW] = 0  #default in case target changed last loop.  We'll check immediately below\n",
    "            if abs(neededWedgePop/targetWedgePop[nW]) < 0.5*toler :  #is this wedge close enough to stop iterating on it?\n",
    "                isSatisfied[nW] = 1\n",
    "            if isOverEdge[nW] == 0 and isSatisfied[nW] == 0:   #we skip over-boundary and near-perfect wedges\n",
    "                wedgePopDelta = wedgePop[nW] - oldWedgePop[nW]  #how much wedgePop gained in last loop\n",
    "                if (wedgePopDelta == 0): #our last wedge change was in an empty area (desert or off map)\n",
    "                    gainAdjust = EulerGain/gain  #Euler method often over-cautious at map edges or in sparse areas\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] * targetWedgePop[nW] / wedgePop[nW]  #Euler guess\n",
    "                    # can't use Newton-Raphson since last dy was zero, use Euler instead\n",
    "                    print(\"we hit a zero wedgePopDelta for tract, loop, wedge, wedgeAngle,r,pop\",t,tractLoopCounter[t],\n",
    "                          nW, round(180./pi*wedgeAngle[nW],1),round(currentR[nW],4),round(wedgePop[nW],1))\n",
    "                else: #use N-R estimation\n",
    "                    gainAdjust = 1.0\n",
    "                    R2delta = currentR[nW]*currentR[nW] - oldR[nW]*oldR[nW]  #this and below are run/rise of current slope\n",
    "                    guessedRsquared = currentR[nW]*currentR[nW] + R2delta / wedgePopDelta * neededWedgePop\n",
    "                    \n",
    "                guessedRsquared = max( guessedRsquared, 0. ) #minimum guardrail\n",
    "                guessed_dr = guessedRsquared ** 0.5 - currentR[nW]  #best guess for wedge radius change\n",
    "                #gain = max(0.3,normalGain ** (1. + yoyoFactor * yoyoCount[nW]))  #obsolete; using bisect\n",
    "                dr = gain * gainAdjust * guessed_dr   #gain typically ~0.8 for stability\n",
    "                dr = max( -0.5*currentR[nW],min(dr,max_dr[nW]) )  #apply min and max guardrails to wedge radius change\n",
    "                \n",
    "                if np.sign(dr) != np.sign(old_dr[nW]) :  #are we yoyoing? How many times?\n",
    "                    yoyoCount[nW] += 1\n",
    "                if yoyoCount[nW] > 3 and yoyoCount[nW] <= 6 :\n",
    "                    wedgeMaxR[nW] = max(wedgeMaxR[nW],currentR[nW]) #will track largest R in yoyo cycle\n",
    "                    max_dr[nW] = 2.*wedgeMaxR[nW]  #this will now store a reasonable max bisection step size\n",
    "                if yoyoCount[nW] > 6 :  #switch to bisection method; too many yo-yo's\n",
    "                    print(yoyoCount[nW],\"yoyos for tract,wedge,wedgePop,r=\",t,nW,wedgePop[nW],round(currentR[nW],4) )\n",
    "                    max_dr[nW] = 0.5 * max_dr[nW] #we are now bisecting ...\n",
    "                    if(neededWedgePop < 0):\n",
    "                        dr = max(-1.*max_dr[nW], -0.5*currentR[nW])  #reduce wedge size\n",
    "                    else:\n",
    "                        dr = max_dr[nW]  #increase wedge size\n",
    "                old_dr[nW] = dr  #these three lines -- save current as old values for next loop\n",
    "                oldR[nW] = currentR[nW]\n",
    "                oldWedgePop[nW] = wedgePop[nW]\n",
    "                if dr > 0. :  #we are growing a wedge trapezoid piece \n",
    "                    outerR = currentR[nW] + dr\n",
    "                    innerR = currentR[nW]\n",
    "                    currentR[nW] = outerR    #for next loop around\n",
    "                else:    #this wedge trapezoid piece will be SUBTRACTED from current wedge\n",
    "                    outerR = currentR[nW]\n",
    "                    innerR = currentR[nW] + dr  #remember, dr is negative here\n",
    "                    currentR[nW] = innerR             #for next loop around\n",
    "                #now, describe the new wedge to probe for precinct intersections ...\n",
    "                pt1[nW] = Point(tractCP.x+innerR*math.cos(angle[nW]), tractCP.y+innerR*math.sin(angle[nW]) )\n",
    "                pt2[nW] = Point(tractCP.x+outerR*math.cos(angle[nW]), tractCP.y+outerR*math.sin(angle[nW]) )\n",
    "                pt3[nW] = Point(tractCP.x+outerR*math.cos(angle2[nW]),tractCP.y+outerR*math.sin(angle2[nW]) )\n",
    "                pt4[nW] = Point(tractCP.x+innerR*math.cos(angle2[nW]),tractCP.y+innerR*math.sin(angle2[nW]) )\n",
    "                wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW],pt4[nW]])  #true for wedge add-on or to-be-trimmed\n",
    "                \n",
    "                printPoly[nW] = wedgePoly  #for debugging\n",
    "                latestWedgePop = 0.  #for the new piece, not the entire triangle\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                usedPrecinct = [0]*nPrecincts\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    latestWedgePop  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                                    loopTractUse[nTT] += np.sign(dr)*overlap/tractArea[nTT] * tractPop[t]/avgDistrictPop\n",
    "                        # found all possible tract overlaps with this increment / decrement to this wedge.\n",
    "                        \n",
    "                wedgePop[nW] += np.sign(dr)*latestWedgePop  #for full triangle, based on this latest piece\n",
    "                # Now, flag if we're beyond boundary\n",
    "                if wedgePop[nW] < targetWedgePop[nW] : #if growing, confirm we're not beyond MAP boundary\n",
    "                    leadingEdge = LineString([pt2[nW],pt3[nW]])\n",
    "                    if leadingEdge.intersects(MAP) :  #still room to grow in part of edge, keep going\n",
    "                        gerrymandering = \"evil\"  #it had to be said\n",
    "                    else:  #this wedge is fully beyond the map. give up on more map intersection\n",
    "                        isOverEdge[nW] = 1\n",
    "                        shortedWedge = nW  #ID'ing highest numbered wedge that got shorted by the boundary in this loop\n",
    "                        oldWedgePopGap = wedgePopGap[nW]\n",
    "                        wedgePopGap[nW] = targetWedgePop[nW] - wedgePop[nW]  #how far this wedge's pop is below its target\n",
    "                        nActiveWedges -= 1  #a few lines below, we will adjust other wedge's targets\n",
    "                        sumWedgePopGapChange += wedgePopGap[nW] - oldWedgePopGap\n",
    "                        if (nActiveWedges == 0) : #we're doomed, somehow all wedges are short and off map\n",
    "                            print(\"PUNT! no more active wedges for tract, loop =\",t,tractLoopCounter[t] )\n",
    "                            tractLoopCounter[t] = nGiveUp + 1  #PUNT !!!\n",
    "                            \n",
    "                        max_dr = [globalMax_dr]*nWedges   #allow other wedges to take big steps again to catch up\n",
    "                        yoyoCount = [0] * nWedges  #go back to original gains on ALL active wedges\n",
    "                   \n",
    "        # end of nW loop to adjust all wedge populations by growing or trimming wedges, IDing over-edge wedges\n",
    "        tractLoopCounter[t] +=1   # still looping on home district Pop.\n",
    "        nReceivingWedges = nActiveWedges\n",
    "        oppFlag = 0\n",
    "        if 0 == 1: #always false; for HD2 we would have checked here for opp wedge restriction; ignore this block\n",
    "            # targetWedgePop[int(nWedges/2)] < 0.99 * avgDistrictPop/float(nWedges): #is opp wedge restricted?\n",
    "            oppFlag = 1\n",
    "            nReceivingWedges -= 1-isOverEdge[int(nWedges/2)]  #if yes, don't count opposite wedge as a receiver\n",
    "        if nReceivingWedges == 0: #rarely, the opposite wedge is the only active one\n",
    "            for nWW in range(nWedges): #in this case, we allow the opposite wedge to pick up the slack\n",
    "                targetWedgePop[nWW] += sumWedgePopGapChange/max(1.,float(nReceivingWedges) )\n",
    "        else: #if the opp wedge has a ceiling wedgePopTarget, distribute wedgePopGap to other active wedges\n",
    "            for nWW in range(nWedges):  #time to make adjustments in target wedge pops based on boundary fails\n",
    "                if nWW != int(nWedges/2) or oppFlag == 0 :  #excluding flagged opposite wedge as a receiver\n",
    "                    targetWedgePop[nWW] += sumWedgePopGapChange/float(nReceivingWedges)\n",
    "\n",
    "        # *** NEW 1/19/22 CODE TO ADJUST WEDGE ANGLES WHEN A BOUNDARY ENCOUNTERED\n",
    "        if (nActiveWedges == nWedges or nActiveWedges < nWedges - 2\n",
    "           or didWeRestart[t] == 1) :  #0 or 3+ over-boundary short wedges, or we've already adjusted wedge angles once\n",
    "            HDpop = np.sum(wedgePop) #Keep going with normal wedge growth and trim process\n",
    "        else :  # 1 OR 2 SHORTED WEDGES.  MAY WANT TO ALTER WEDGE ANGLES\n",
    "            didWeRestart[t] = 1  #to ensure we adjust wedge angles and target wedgePops at most once per tract\n",
    "            if (nActiveWedges == nWedges - 2) :  #exactly two wedges got shorted in this loop -- are they opposing?\n",
    "                oppW = int( (shortedWedge + nWedges/2) % nWedges)  #the index of wedge opposite the last shorted wedge\n",
    "                if (isOverEdge[oppW] == 0) :\n",
    "                    print(\"we have 2 non-opposing shorted wedges for tract no\",t)\n",
    "                    totalLiveAngle = 2.*pi - np.sum(isOverEdge)*avgWedgeAngle  #avail angle to divvy among live wedges  \n",
    "                    for nW in range (nWedges) : #Decrease wedge angles opposite shorted wedges via a complex weighting\n",
    "                        if isOverEdge[nW] == 0 :\n",
    "                            oppW = int( (nW + nWedges/2) % nWedges) \n",
    "                            angleWeight = (np.sum(wedgePopGap)-wedgePopGap[oppW])/np.sum(wedgePopGap)/(nActiveWedges-1)\n",
    "                            wedgeAngle[nW] = angleWeight * totalLiveAngle  \n",
    "                else : #shorted wedges ARE opposing\n",
    "                    print(\"we have 2 OPPOSING shorted wedges for tract no\",t) # no wedge-angle adjustment in this case\n",
    "                    #wedgeAngle=[avgWedgeAngle]*nWedges  #comment this out, no change\n",
    "            else: # a single shorted wedge (over boundary).  COMPLETELY RESTART wedge growth process\n",
    "                shortW = shortedWedge #Next dozen lines: convoluted code to find angle to closest boundary point\n",
    "                #print(\"1 wedge over map boundary for tract\",t,\"at loop, wedgePop\",tractLoopCounter[t],wedgePop[shortW] )\n",
    "                shortedPoly = Polygon([tractCP, pt2[shortW],pt3[shortW] ])\n",
    "                MAPedge = shortedPoly.intersection(MAP.exterior) #true state boundary line where wedge crossed it\n",
    "                minDistance = max(tractCP.distance(MAPedge),tinyR)  #closest distance to edge where wedge hit the boundary\n",
    "                edgeCircle = tractCP.buffer(1.01*minDistance)  #build a circle a little bigger than this distance\n",
    "                #  1.001 is not big enough; will occasionally not intersect\n",
    "                closeArea = edgeCircle.intersection(MAPedge)\n",
    "                counter = 0.\n",
    "                maxCounter = 10. #give up after 10 tries\n",
    "                while closeArea.is_empty :  # protect against missing the map boundary somehow\n",
    "                    print(\"need to widen edge circle beyond\",minDistance, \"for tract (x,y)=(\",tractCP.x,tractCP.y)\n",
    "                    minDistance = minDistance * 1.1\n",
    "                    edgeCircle = tractCP.buffer(minDistance)  #widen the circle\n",
    "                    closeArea = edgeCircle.intersection(MAPedge)\n",
    "                    counter += 1\n",
    "                    if counter >= maxCounter: #not finding map edge intersection.  Try another way\n",
    "                        print(\"did not find boundary with circle approach.  Brute-force it.\")\n",
    "                        minDistance = tractCP.distance(MAP.exterior)\n",
    "                        edgeCircle = tractCP.buffer(1.01*minDistance)\n",
    "                        closeArea = edgeCircle.intersection(MAP.exterior)                        \n",
    "            \n",
    "                closePoint = closeArea.centroid  # this is a point very close to the closest beeline from tract to map edge\n",
    "                #print(closePoint,tractCP, \"are boundary point and tract center point\")\n",
    "                x1 = closePoint.x\n",
    "                x2 = tractCP.x  #debugging\n",
    "                dx = closePoint.x - tractCP.x\n",
    "                dy = closePoint.y - tractCP.y\n",
    "                exitAngle = pi/2. * np.sign(dy)  #default in case dx=0\n",
    "                if (dx != 0 ):\n",
    "                    exitAngle = math.atan(dy/dx)  #this is the angle from the x-axis to the exit beeline\n",
    "                    if dx < 0 :  #use complementary atan solution; boundary is west of tract centroid\n",
    "                        exitAngle = pi + exitAngle\n",
    "                angle0[t] = exitAngle # this reorients 0th wedge to face boundary's closest point               \n",
    "                # New 1/23/22 - let's estimate wedgePopGap at ideal orientation, normal angle   # ****************\n",
    "                wedgeAngle1 = exitAngle - 0.5*avgWedgeAngle\n",
    "                wedgeAngle2 = exitAngle + 0.5*avgWedgeAngle\n",
    "                maxR = max(stateWidth,stateHeight) #ensuring we make a big enough wedge\n",
    "                wedgePt1 = tractCP\n",
    "                wedgePt2 = Point(tractCP.x+maxR*math.cos(wedgeAngle1),  tractCP.y+maxR*math.sin(wedgeAngle1) )\n",
    "                wedgePt3 = Point(tractCP.x+maxR*math.cos(wedgeAngle2), tractCP.y+maxR*math.sin(wedgeAngle2) )\n",
    "                wedgePoly = Polygon([ wedgePt1, wedgePt2, wedgePt3 ])\n",
    "                minWedgePop[t] = 0. #the wedgePop of the shorted wedge if we re-orient but don't adjust angles\n",
    "                usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "                for nG in range(nGrids) :  # loop over ACTIVE grids to look for intersecting tracts\n",
    "                    gridIntersxn = gridGeom[nG].intersection(wedgePoly)\n",
    "                    if (gridIntersxn.area > 0) :  #this grid is RELEVANT to this wedge\n",
    "                        for tt in range(nGridTracts[nG]) : #look for intersxns with all tracts in this grid\n",
    "                            nTT = gridTractNo[nG][tt] #shorthand for this tract's global tract no\n",
    "                            if(usedTract[nTT] == 0) :  #Did we not already look at this tract in another grid list?\n",
    "                                usedTract[nTT] = 1  #well, now we have!  Probe intersection with wedge\n",
    "                                overlap = tractGeom[nTT].intersection(wedgePoly).area\n",
    "                                if overlap > 0 :\n",
    "                                    fracArea = overlap/tractArea[nTT]\n",
    "                                    minWedgePop[t]  += fracArea*tractPop[nTT]  #always positive (used in density calc)\n",
    "                #print(\"the minimum shorted Wedge Pop for tract {0} is {1}\".format(t,minWedgePop[t]) )  #debug\n",
    "                #printAngle[t] = round(exitAngle*180./pi,4) #for debugging\n",
    "                printDist = round(minDistance,4)\n",
    "                #print(t,\"(\",tractCP.x,tractCP.y,\")\",printAngle,printDist,\"t,tract(x,y),exitAngle,dist\")\n",
    "                # ********************************************************************\n",
    "                # assign new wedge angles.  The two wide angles face toward and away from nearest boundary\n",
    "                #ratio = wedgePopGap[shortW]/ (avgDistrictPop/nWedges)  #0-1; this is degree of shortness\n",
    "                ratio = 1. - minWedgePop[t]/ (avgDistrictPop/nWedges)  # *** UPDATED\n",
    "                # printWedgePopGap[t] = wedgePopGap[shortW]\n",
    "                wideAngle = (1. + coeff1 * ratio + coeff2 * ratio*ratio)*avgWedgeAngle #HERE, WE ADJUST ANGLES\n",
    "                wideAngle = min(wideAngle, maxWideAngle*pi) #set an upper limit\n",
    "                thinAngle = (2.*pi - 2.*wideAngle)/(nWedges - 2.)  #other wedges equally angle-compressed\n",
    "                #if (t % 9 == 0):  #occasional print\n",
    "                    #print(\"tract,wide, thin angles are \",t,round(180/pi*wideAngle,4),round(180/pi*thinAngle,4) )\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0 or nW == int(nWedges/2) ) : #assign these two as shorted wedge and its opposite\n",
    "                        wedgeAngle[nW] = wideAngle\n",
    "                    else :\n",
    "                        wedgeAngle[nW] = thinAngle\n",
    "                #** COMPLETELY RESTART WEDGE GROWTH PROCESS FROM INITIAL PIZZA SLICES, NOW RE-ORIENTED\n",
    "                loopTractUse = [0.] * nTracts  # reset as we are starting over\n",
    "                loopPrecinctUse = [0.]* nPrecincts\n",
    "                HDpop = 0.\n",
    "                nActiveWedges = nWedges\n",
    "                targetWedgePop = [avgDistrictPop/nWedges] * nWedges\n",
    "                oldR = [0.]*nWedges   #for remembering last loop's wedge radius  \n",
    "                currentR = [tinyR]*nWedges                \n",
    "                old_dr = [tinyR]*nWedges  #this will track the last radial step (to help convergence)\n",
    "                max_dr = [globalMax_dr]*nWedges  #reset max step size\n",
    "                wedgePop = [0.]*nWedges\n",
    "                oldWedgePop = [0.]*nWedges #wedge pop from previous round\n",
    "                wedgePopGap = [0.]*nWedges\n",
    "                isOverEdge = [0] *nWedges\n",
    "                isSatisfied = [0] *nWedges\n",
    "                yoyoCount = [0] *nWedges #just in case we missed this earlier\n",
    "                wedgeMaxR = [0.] * nWedges #resetting\n",
    "                angle[0] = angle0[t] - 0.5*wideAngle  #the 0th wedge always faces the boundary\n",
    "                angle2[0] = angle[0] + wedgeAngle[0]\n",
    "                for nW in range(nWedges) :\n",
    "                    if (nW == 0):\n",
    "                        angle[nW] = angle0[t] - 0.5*wideAngle  #we have to start somewhere :-)\n",
    "                    else :\n",
    "                        angle[nW] = angle2[nW-1]\n",
    "                    angle2[nW] = angle[nW]+wedgeAngle[nW]      \n",
    "                    pt1[nW] = tractCP\n",
    "                    pt2[nW] = Point(tractCP.x+tinyR*math.cos(angle[nW]),  tractCP.y+tinyR*math.sin(angle[nW]) )\n",
    "                    pt3[nW] = Point(tractCP.x+tinyR*math.cos(angle2[nW]), tractCP.y+tinyR*math.sin(angle2[nW]) )\n",
    "                    wedgePoly = Polygon([pt1[nW], pt2[nW], pt3[nW] ])\n",
    "                    wedgePop[nW] = tractPop[t]* wedgePoly.area/max(tractArea[t],minTractArea)\n",
    "                HDpop = np.sum(wedgePop)   #** END OF RESTART BLOCK.  RE-ENTER MAIN LOOP FOR THIS TRACT\n",
    "                      \n",
    "        # **** BELOW IS TO CORRECT FOR NONCONVERGENCE *****\n",
    "        if (tractLoopCounter[t] > nLoopPrint):  #may be becoming unstable. Alert user,    #and (don't) reduce gain\n",
    "            strPop = str(round(HDpop,2))\n",
    "            strWdg = \"Overedge?\"\n",
    "            strSat = \"Satisfied?\"\n",
    "            strYoyo = \"yoyo?\"                \n",
    "            strTWP_dr = \"tWP,dr\"\n",
    "            for nWW in range(nWedges) :\n",
    "                strPop = strPop + \", \"+str(round(wedgePop[nWW],1) )\n",
    "                strWdg = strWdg +str(isOverEdge[nWW])+\", \"\n",
    "                strSat = strSat +str(isSatisfied[nWW])\n",
    "                strYoyo = strYoyo + str(yoyoCount[nWW])\n",
    "                strTWP_dr = strTWP_dr + \", \"+str(round(targetWedgePop[nWW],1) )+\",\"+str(round(old_dr[nWW],4) )\n",
    "            print(\"loop{0}, tr{1},wedgePops{2}, {3},{4},{5} \".format(tractLoopCounter[t],t,strPop,strWdg,strSat,strYoyo) )\n",
    "            print(\"   targetWP, latest drx4 are\",strTWP_dr)\n",
    "\n",
    "        if (tractLoopCounter[t] > nLoopSuperPrint) :\n",
    "            for nWW in range(nWedges):                \n",
    "                print(\"wedge no, currentR,oldWedgePop, wedgePop, overEdge?\",nWW,str(round(currentR[nWW],5)), \n",
    "                      str(round(oldWedgePop[nWW],4)), str(round(wedgePop[nWW],4)),isOverEdge[nWW])   #debug\n",
    "                x, y = printPoly[nWW].exterior.xy     #wedge debugging .....\n",
    "                plt.plot(x, y, c=(0.1, 0.2, 0.02+float(nWW)/nWedges) )\n",
    "            plt.show()\n",
    "        if(tractLoopCounter[t] >= nGiveUp):\n",
    "            print(\"I looped {0} times on tract {1}, giving up w pop {2}\".format(nGiveUp,t,HDpop) )\n",
    "            wrongPop[t] = HDpop  #this will flag this HD as triaged\n",
    "            HDpop = avgDistrictPop  #white lie to kick out of loop\n",
    "        # *** END OF TRIAGE FOR NONCONVERGENCE\n",
    "        \n",
    "    # END OF WHILE LOOP --> we are within tolerance of avgDistrictPop. Finalize this tract's Home District stats\n",
    "    for nTT in range (nTracts) :\n",
    "        tractUse[nTT] += loopTractUse[nTT]\n",
    "    totGOP = 0.\n",
    "    totVote = 0.\n",
    "    totVAP = 0.\n",
    "    totHisp = 0.\n",
    "    totBlack = 0.  #zero these out prior to summing over final wedges\n",
    "    usedTract = [0]*nTracts  #rezero lists of tracts and precincts that could straddle multiple grids\n",
    "    usedPrecinct = [0]*nPrecincts\n",
    "\n",
    "    if (tractPop[t] < minTractPop or tractArea[t] == 0 or isSkippedTract[t] == 1): \n",
    "        #we bypassed the big loop; this tract is inconsequential\n",
    "        HDvPop[t] = HDpop  #a white lie\n",
    "        HDweight[t] = 0.000001  #to suppress this in total stats\n",
    "    else :\n",
    "        HDvPop[t] = np.sum(wedgePop)\n",
    "        # HDvHisp[t] = np.sum(wedgeHisp)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        # HDvBlack[t] = np.sum(wedgeBlack)/np.sum(wedgePop)  #3/2/22 - move to final wedge calcs\n",
    "        HDweight[t] = tractPop[t]/np.sum(tractPop)\n",
    "        nearEdge[t] = nWedges - nActiveWedges  #flagging the number of wedges that were not completely pop-filled\n",
    "        centerPt = tractCP\n",
    "        outerPt2 = Point(tractCP.x+currentR[0]*math.cos(angle[0]), tractCP.y+currentR[0]*math.sin(angle[0]) )\n",
    "        outerPt3 = Point(tractCP.x+currentR[0]*math.cos(angle2[0]),tractCP.y+currentR[0]*math.sin(angle2[0]) )\n",
    "        HDpolly = Polygon([centerPt,outerPt2,outerPt3])   #initiate a polygon that will be the home district\n",
    "        for nW in range(nWedges):\n",
    "            HDradius[t][nW] = currentR[nW]\n",
    "            HDangle[t][nW] = angle[nW]\n",
    "            \n",
    "        for nW in range(1,nWedges):  #save final geometry, compute minority and partisan stats\n",
    "            cR = currentR[nW] #shorthand\n",
    "            outerPt2 = Point(tractCP.x+cR*math.cos(angle[nW]), tractCP.y+cR*math.sin(angle[nW]) )\n",
    "            outerPt3 = Point(tractCP.x+cR*math.cos(angle2[nW]),tractCP.y+cR*math.sin(angle2[nW]) )\n",
    "            HDpolly =  HDpolly.union( Polygon([centerPt,outerPt2,outerPt3]) )  #add this wedge to HD district polygon\n",
    "        HDpolly = HDpolly.intersection(MAP)  #exclude map's convex hull and buffer, just the original union of precincts\n",
    "        HDarea[t] = HDpolly.area  #for stats, final Home District area\n",
    "        for nG in range(nGrids) :  # ID the ACTIVE grids to look for intersecting precincts\n",
    "            gridIntersxn = gridGeom[nG].intersection(HDpolly)\n",
    "            if (gridIntersxn.area > 0) :  #this grid is RELEVANT to the final Home District\n",
    "                for tt in range(nGridTracts[nG]):\n",
    "                    nTT = gridTractNo[nG][tt]\n",
    "                    if usedTract[nTT] == 0: #only examine tracts that have not already been called for intersection\n",
    "                        usedTract[nTT] == 1  #this tract has now been called \n",
    "                        overlap = tractGeom[nTT].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/tractArea[nTT]\n",
    "                            totVAP += fracArea*tractVAP[nTT]\n",
    "                            totHisp += fracArea*tractHisp[nTT]\n",
    "                            totBlack += fracArea*tractBlack[nTT]\n",
    "                        \n",
    "                for pp in range(nGridPrecincts[nG]): #scanning the list of precincts in this active grid\n",
    "                    nPP = gridPrecinctNo[nG][pp]\n",
    "                    if usedPrecinct[nPP] == 0  :\n",
    "                        usedPrecinct[nPP] = 1  #don't double up on precinct intersection\n",
    "                        overlap = vtdGeom[nPP].intersection(HDpolly).area\n",
    "                        if overlap > 0 :\n",
    "                            fracArea = overlap/vtdArea[nPP]\n",
    "                            totGOP += fracArea*vtdGOP[nPP]*vtdPop[nPP]\n",
    "                            totVote += fracArea*vtdPop[nPP]\n",
    "                            loopPrecinctUse[nPP] += overlap/vtdArea[nPP] *tractPop[t]/avgDistrictPop\n",
    "        HDvHisp[t] = totHisp/totVAP\n",
    "        HDvBlack[t] = totBlack/totVAP\n",
    "        HDvGOP[t] = totGOP / totVote\n",
    "        for nPP in range (nPrecincts) :\n",
    "            precinctUse[nPP] += loopPrecinctUse[nPP] #add to global use for this precinct\n",
    "            \n",
    "        \n",
    "\n",
    "    # end of loop on this tract\n",
    "for t in range(nTracts):\n",
    "    if(wrongPop[t] > 0):\n",
    "        HDvPop[t] = wrongPop[t]  #undo the lie that got us out of the loop early\n",
    "HDsumWeight = np.sum(HDweight)\n",
    "print(\"Sum of weights over all precinct home districts should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea) )\n",
    "stateGOP2 = 0.\n",
    "stateHisp2 = 0.\n",
    "stateBlack2 = 0.\n",
    "for t in range(nTracts):\n",
    "    HDweight[t] = HDweight[t]/HDsumWeight   #renormalizing\n",
    "    stateGOP2 += HDweight[t]*HDvGOP[t]\n",
    "    stateBlack2 += HDweight[t]*HDvBlack[t]\n",
    "    stateHisp2 += HDweight[t]*HDvHisp[t]\n",
    "statePop = np.sum(tractPop)\n",
    "print(\"calculated statewide vote was {0}, should have been {1}\".format(stateGOP2, stateGOP) )\n",
    "print(\"calcd statewide Hispanic pop was {0}, should have been {1}\".format(stateHisp2, np.sum(tractHisp)/stateVAP ) )\n",
    "print(\"calcd statewide Black pop was {0}, should have been {1}\".format(stateBlack2, np.sum(tractBlack)/stateVAP ) )\n",
    "print(\"fraction of HDs that with altered wedge angles near boundaries = \",np.sum(didWeRestart)/nTracts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "24a347a9-8220-4c1a-bcfc-14923f1f98f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sum of weights over all precinct home districs should have been 1.000, but was  1.000327175953632\n",
      "Average and max number of wedgePop loops per tract were:  5.657275439177991 40.0\n",
      "min,average,max of Home District areas were:  0.0 0.7049047700215471 4.104812282753314 for  MI\n"
     ]
    }
   ],
   "source": [
    "#use this line to document progress\n",
    "# start MI 4:48pm, end 5:45.  restart (13 districts, not 14) at 7:20, end 8:23\n",
    "print(\"Sum of weights over all precinct home districs should have been 1.000, but was \",HDsumWeight)\n",
    "print(\"Average and max number of wedgePop loops per tract were: \",np.average(tractLoopCounter),np.max(tractLoopCounter) )\n",
    "print(\"min,average,max of Home District areas were: \",np.min(HDarea),np.average(HDarea),np.max(HDarea),\"for \",STATE )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "72247942-899a-4ab2-9796-239aa1e524fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MI 19MarB\n"
     ]
    }
   ],
   "source": [
    "date = \"19MarB\"\n",
    "print(STATE, date)  #check that I will not overwrite existing file :-)\n",
    "tractCPx = [0.]*nTracts\n",
    "tractCPy = [0.]*nTracts\n",
    "tractNo = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    tractCPx[t]=tractGeom[t].centroid.x\n",
    "    tractCPy[t]=tractGeom[t].centroid.y\n",
    "    tractNo[t]=t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "c55ac5fd-7402-47b9-9093-10b9fe0912b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#LET'S WRITE AN OUTPUT FILE BEFORE WE FORGET :-)\n",
    "#convert HD wedge geometries to 1D arrays.  BELOW IS FOR 4-WEDGE.  CAN AUGMENT FOR 6-WEDGE\n",
    "HDangle0 = [0.]*nTracts\n",
    "HDradius0 = [0.]*nTracts\n",
    "HDangle1 = [0.]*nTracts\n",
    "HDradius1 = [0.]*nTracts\n",
    "HDangle2 = [0.]*nTracts\n",
    "HDradius2 = [0.]*nTracts\n",
    "HDangle3 = [0.]*nTracts\n",
    "HDradius3 = [0.]*nTracts\n",
    "for t in range(nTracts):\n",
    "    HDangle0[t] = HDangle[t][0]\n",
    "    HDradius0[t] = HDradius[t][0]\n",
    "    HDangle1[t] = HDangle[t][1]\n",
    "    HDradius1[t] = HDradius[t][1]\n",
    "    HDangle2[t] = HDangle[t][2]\n",
    "    HDradius2[t] = HDradius[t][2]\n",
    "    HDangle3[t] = HDangle[t][3]\n",
    "    HDradius3[t] = HDradius[t][3]\n",
    "# now convert output to pandas dataframe and export\n",
    "\n",
    "paramList = [\"STATE\",\"stateGOP\",\"nDistricts\",\"nTracts\",\"nPrecincts\",\"nWedges\",\"popn-toler\",\n",
    "             \"gain\",\"coeff1\",\"coeff2\"]\n",
    "paramValues = [STATE,stateGOP, nDistricts, nTracts,nPrecincts,nWedges, toler, gain, coeff1,coeff2]\n",
    "for i in range(nTracts-len(paramList)):\n",
    "    paramList.append(\".\")\n",
    "    paramValues.append(-99)  #so all columns have same number of entries, even the parameter list\n",
    "df = pd.DataFrame( {\"paramList\": paramList,\"paramValues\":paramValues,\"HD-pop\":HDvPop,\"HDvGOP\":HDvGOP,\"HDvHisp\":HDvHisp,\n",
    "                    \"HDvBlack\":HDvBlack,\"HDwt\":HDweight,\"HDarea\":HDarea, \"HDangle0\":HDangle0, \"HDangle1\":HDangle1,\n",
    "                    \"HDangle2\":HDangle2,\"HDangle3\":HDangle3,\"HDradius0\":HDradius0,\"HDradius1\":HDradius1,\n",
    "                    \"HDradius2\":HDradius2,\"HDradius3\":HDradius3,\"startAngle\":angle0,\"tractNo\":tractNo,\n",
    "                    \"Loops\":tractLoopCounter,\"tractPop\":tractPop,\"tractHisp\":tractHisp,\"tractBlack\":tractBlack,\n",
    "                    \"centroid x\":tractCPx,\"centroid y\":tractCPy, \"tractUse\":tractUse,\"nearEdge\":nearEdge,\n",
    "                    \"wrongPop\":wrongPop,\"Restart?\":didWeRestart} ) \n",
    "\n",
    "outname = STATE+str(nDistricts)+\"HD1tol\"+str(toler)+\"nW\"+str(nWedges)+date+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname\n",
    "df.to_csv(outpath)\n",
    "precinctNo = [0.]*nPrecincts\n",
    "vtdX = [0.]*nPrecincts\n",
    "vtdY = [0.]*nPrecincts\n",
    "for p in range(nPrecincts):\n",
    "    precinctNo[p]=p\n",
    "    vtdX[p] = vtdGeom[p].centroid.x\n",
    "    vtdY[p] = vtdGeom[p].centroid.y\n",
    "df2 = pd.DataFrame( {\"precinctNo\":precinctNo,\"precinctPop\":vtdPop,\"precUse\":precinctUse, \"vtdX\":vtdX, \"vtdY\":vtdY} )\n",
    "outname2 = STATE+date+\"_VTD_tol\"+str(toler)+\"nW\"+str(nWedges)+\".csv\"\n",
    "outpath = \"state_HD_output/\"+outname2\n",
    "df2.to_csv(outpath)  #currently, I'm outputting the precinct use stats to a separate file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "c4449da0-47a9-40cc-a585-8288447290e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2650 0.08 ( -83.95989307060574 43.955727130759065 ) 0.6762073851021959 t, pop/target,(x,y), pctR\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we will redo tract numbers  [2650]\n"
     ]
    }
   ],
   "source": [
    "# Looking for nonconvergers ...  Run even if none observed, just to check  \n",
    "redo = [0]\n",
    "counter = 0.\n",
    "for t in range (nTracts) :\n",
    "    if wrongPop[t] > 0 :\n",
    "        ratio = round(wrongPop[t]/avgDistrictPop,2)\n",
    "        tractX = tractGeom[t].centroid.x\n",
    "        tractY = tractGeom[t].centroid.y        \n",
    "        print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        if (ratio < 0.9 or ratio > 1.1) :  #these we will redo\n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=14)\n",
    "            if counter == 0 :  #first redo\n",
    "                redo = [t]\n",
    "            else :\n",
    "                redo.append(t)\n",
    "            counter+= 1\n",
    "        else: #show these milder offenders on the map in a smaller font            \n",
    "            plt.text(tractGeom[t].centroid.x, tractGeom[t].centroid.y,ratio, fontsize=9)\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()\n",
    "print(\"we will redo tract numbers \",redo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "be6e965b-4ca8-4af2-8031-63132fbb939d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4659 0.29591411562792785 1526 pop,GOP 0.6585845347313237\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting underused PRECINCTS\n",
    "for p in range (nPrecincts) :\n",
    "    if precinctUse[p] < 0.6 and isSkippedPrecinct[p] == 0 and vtdPop[p] > 0: #ignore suppressed precincts\n",
    "        print(p,precinctUse[p], vtdPop[p],\"pop,GOP\",vtdGOP[p])\n",
    "        pU = round(precinctUse[p],3)\n",
    "        tractX = vtdGeom[p].centroid.x\n",
    "        tractY = vtdGeom[p].centroid.y        \n",
    "        # print(t, ratio,\"(\",tractX,tractY,\")\",HDvGOP[t], \"t, pop/target,(x,y), pctR\")\n",
    "        plt.text(vtdGeom[p].centroid.x, vtdGeom[p].centroid.y,pU, fontsize=9)\n",
    "        if notPolyVTD[p] == 0 :\n",
    "            x,y = vtdGeom[p].exterior.xy\n",
    "            plt.plot(x,y)\n",
    "        else :\n",
    "            for geom in vtdGeom[p].geoms:\n",
    "                x,y = geom.exterior.xy\n",
    "                plt.plot(x,y)\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"purple\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "37f47a77-d58e-4b4c-be60-676819128e21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (OPTIONAL) IF WE STOPPED AND RESTARTED, read the data back in:  #NEED TO UPDATE THIS W/ANGLES, RADII\n",
    "import pandas as pd\n",
    "infilename = \"FL_nD28tol0.01nW4.csv\"\n",
    "df2 = pd.read_csv(\"state_HD_output/\"+infilename)\n",
    "HDvPop = df2[\"HD-pop\"]\n",
    "HDvHisp = df2[\"HDvHisp\"]\n",
    "HDvGOP = df2['HDvGOP']\n",
    "HDweight = df2['HDwt']\n",
    "HDarea = df2['HDarea']\n",
    "tractLoopCounter = df2['Loops']\n",
    "tractPop = df2['tractPop']\n",
    "tractCPx = df2['centroid x']\n",
    "tractCPy = df2['centroid y']\n",
    "tractUse = df2['tractUse']\n",
    "# nearEdge = df2['nearEdge']   #add this back in, PLEASE\n",
    "df2.head()\n",
    "nTracts = len(tractPop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e9b2c0c0-fb53-44e5-9155-1963d4833ee8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "750596.7418101031 0.1131774380110593 0.0001361943772 0.0400616118781349 0.7450137754374908\n"
     ]
    }
   ],
   "source": [
    "#  HERE'S A BLOCK TO OVERWRITE THE REDONE DATA\n",
    "for r in range(nRedos):\n",
    "    t = redo[r]\n",
    "    HDvPop[t] = redoHDvPop[r]\n",
    "    HDvGOP[t] = redoHDvGOP[r]\n",
    "    tractArea[t] = redoHDarea[r]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c80e0880-6811-4408-b847-6d70bd2546ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt   #SKIP THIS BLOCK IF STARTING FROM FIRST BLOCK\n",
    "from scipy.stats import norm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c8f73bb9-e36b-41c7-9220-1ec09169aa3e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all done with redo's.  Let's look at some outputs.  First, red/blue by home district  SHOULD BE MIX OF RED AND BLUE\n",
    "nPlot = 5\n",
    "for t in range(nTracts):\n",
    "    if(t % nPlot == 0 and tractPop[t] > minTractPop):\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,color='green')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "a5d3b510-6d1e-49ae-9a5d-03352e2b449e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "map of tracts that were used less than 0.7 of expectation in MI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now, where are those UNDERPERFORMING tracts (<0.x usage)\n",
    "maxPlot = 0.70\n",
    "print(\"map of tracts that were used less than {0} of expectation in {1}\".format(maxPlot, STATE) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] < maxPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy   #turn these on if I pull map back in\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "876011e8-d37e-44a6-8dd9-a1e4d2f51e58",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of tracts that were used more than 1.2 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the OVERUSED tracts?\n",
    "minPlot = 1.2\n",
    "print(\"here is a map of tracts that were used more than {0} of expectation\".format(minPlot) )\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "19bfa7c8-0449-4182-8ed9-f5a1c1276f03",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here is a map of 0 tracts that were used more than 1.6 of expectation\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ... And where are the REALLY OVERUSED tracts?\n",
    "minPlot = 1.6\n",
    "counter = 0\n",
    "for t in range(nTracts):\n",
    "    if(tractUse[t] >minPlot and tractUse[t] > 0.05):  #ignore skipped tracts\n",
    "        redd = min(max( 0, ( HDvGOP[t] - 0.5) * 3.0 ),1)\n",
    "        bluu = min(max( 0, (0.5 - HDvGOP[t]) * 3.0 ),1)\n",
    "        plt.scatter(tractCPx[t],tractCPy[t],marker='.',color=(redd, 0,bluu ) )\n",
    "        counter +=1\n",
    "\n",
    "print(\"here is a map of {0} tracts that were used more than {1} of expectation\".format(counter, minPlot) )\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "f3e9bde3-06f6-402d-bce1-62b5631e8db0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION OF TRACT UNDER-USAGE IN OTHER TRACT'S HOME DISTRICTS vs. being near a boundary\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractUse,nearEdge,marker='.' )\n",
    "ax.set(xlabel=\"tract use vs. expectation\", ylabel=\"number of unfilled wedges - max = \"+str(nWedges))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "6bb5c12a-a6a4-4cb1-9026-d227bc11c2ea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11176379762389584 = MI std dev of TRACT usage.  Here is its weighted histogram\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR tract usage in a histogram\n",
    "n_bins=50\n",
    "avgTractUse = 0.\n",
    "statePop = np.sum(tractPop)\n",
    "for t in range(nTracts):\n",
    "    avgTractUse += tractUse[t] * tractPop[t]/statePop\n",
    "sumVarTractUse = 0.\n",
    "for t in range(nTracts):\n",
    "    sumVarTractUse += (tractUse[t]-avgTractUse)**2 *tractPop[t]/statePop\n",
    "sdTractUse = sumVarTractUse ** 0.5\n",
    "\n",
    "print(sdTractUse,\"=\",STATE,\"std dev of TRACT usage.  Here is its weighted histogram\")\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractUse, bins=n_bins, weights=tractPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "121f9869-3d25-4594-93b3-64a57a95aca8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.11312826158670193 = std dev of PRECINCT usage.  Here is its weighted histogram\n",
      "this is a histogram of PRECINCT usage by precinct for MI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR precinct usage in a histogram  \n",
    "n_bins=50\n",
    "avgPrecinctUse = 0.\n",
    "stateVTDPop = np.sum(vtdPop)\n",
    "for p in range(nPrecincts):\n",
    "    avgPrecinctUse += precinctUse[p] * vtdPop[p]/stateVTDPop\n",
    "sumVarPrecinctUse = 0.\n",
    "for p in range(nPrecincts):\n",
    "    sumVarPrecinctUse += (precinctUse[p]-avgPrecinctUse)**2 *vtdPop[p]/stateVTDPop\n",
    "sdPrecinctUse = sumVarPrecinctUse ** 0.5\n",
    "\n",
    "print(sdPrecinctUse,\"= std dev of PRECINCT usage.  Here is its weighted histogram\")\n",
    "print(\"this is a histogram of PRECINCT usage by precinct for\",STATE)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(precinctUse, bins=n_bins, weights=vtdPop)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f6f03dcb-c722-4789-b47f-fb7aa3be97bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "here's a look at numerical stability - number of loops, avg =  6.3687\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avgTLC = 0.\n",
    "usedTracts = 0.\n",
    "for t in range (nTracts):\n",
    "    if(tractLoopCounter[t] > 0):\n",
    "        avgTLC += tractLoopCounter[t]\n",
    "        usedTracts +=1.\n",
    "avgTLC = round(avgTLC / usedTracts, 4)\n",
    "print(\"here's a look at numerical stability - number of loops, avg = \",avgTLC)\n",
    "n_bins=20     \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(tractLoopCounter, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "974631b2-10ef-4847-af2e-a1e26b6fa768",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district pct GOP by tract\n",
      "statewide vote is off by 1e-05 0.48587 HD avg vs true 0.48587\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT red-blue lean in a histogram  and check the statewide vote vs. HD average\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district pct GOP by tract\") \n",
    "print(\"statewide vote is off by\",round((stateGOP2-stateGOP),5),round(stateGOP2,5),\"HD avg vs true\",round(stateGOP,5))\n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvGOP, bins=n_bins,weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "720d9eb5-11e8-45f7-b402-a0048e9ddb99",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "calculated statewide Hispanic pct was 0.0441, should have been 0.04645\n",
      "this is a histogram of home-district VAP pct Hispanic by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR home district pct Hispanic in a histogram\n",
    "print(\"calculated statewide Hispanic pct was {0}, should have been {1}\"\n",
    "      .format(round(stateHisp2,5), round(np.sum(tractHisp)/np.sum(tractVAP),5) ) )\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district VAP pct Hispanic by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDvHisp, bins=[0.1,0.15,0.2,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.0],\n",
    "        weights=HDweight)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "1ab7704f-a226-45a9-98a6-d0684522c1eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Hispanic for MI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF HISPANIC VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT HISPANIC VOTE?\n",
    "n_bins = 20\n",
    "HispSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvHisp[t]*n_bins)\n",
    "    HispSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Hispanic for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,HispSeats,width=0.09 )\n",
    "#plt.scatter(binMid,HispSeats )\n",
    "ax.set(xlabel=\"pct Hispanic\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "3ef49161-368a-40a2-83ff-92187edb3462",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "using a threshold of 0.35 the number of Hisp districts should be 1.409\n"
     ]
    },
    {
     "data": {
      "image/png": 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6AlWAakqpOVrrByo2WuUUfTqaRbGLWH9iPZl5mYzuMNroSEKIYiil0Dh4Addavwy8DKCU6ga86GjFW2vNqmOr+PyPz1mwfwEmZaK5f3PGdRrHs+2fNTqeEKIECvu+Wvqmr8RUSg0GPgT8gKVKqRitde9yS1ZJHEk9wrPLnmX5keV4uXox6bZJvNDxBbyreBsdTQjh4MpUwLXW64B1hc9/AX4p/0iVw7LDy5i2bRorj62kinMVZvSZweNtH5ebDQshKg2ZC6UYa+LW0PeHvtT0qMmU7lN4tPWjMlRQCBtj7ycwQQp4sXLNuQC8cusrjIkcY2wYIcRNs/cZQ+17kORNSM9JZ/zK8ZiUiY5BHY2OI4QQJZIW+DWmb5vOn8l/snDoQjoEdTA6jhDiJtn7EEKQFvjfnMs6B0C72g43P5cQdkeGETqAxIuJLD+ynGVHlrH08FIigyLlpKUQotKz2wKeZ84j9lwse5L2EHc+jhNpJziZfpJLuZdQSqFQKKVIzUplT9IeAGp71WZY02FM6jrJ7udQEELYPrsq4Nn52Xyw7QN+OfgLMWdiikaTAPh7+lO3Wl283LzQWqPRWCwWAqsGMrzZcO4Mu5MWAS3s/qy1EI5ChhHaCLPFzKpjq3hx5YvsTd5LZFAkozuMpmVAS1oGtqSBTwPcXdyNjimEsDJ7b5DZfAGPT4un45cdOX3xNIFVA1l6/1L6NuxrdCwhhKhwNt/RG3c+jtMXTwPQLaQbbWq1MTiREEJYh80X8K4hXVn70FpGdxjNT/t/4o7v7nCIvi8hxI3Zey2w+S4UKGh5RwZFsil+E0fPH0Wj7X78pxDi+vIt+Tib7KLElchuvt2U9VOIToxm9qDZMgRQCEGOOQc3ZzejY1Qou6l01dyqAdDAp4HBSYQQlUF2frbdT/9ssy1wrTW7zuxicexi9qfs59C5Q0DBDRg6B3c2OJ0Qwmg55hzcnOy7BW6TBXzDiQ08v+J5/kj8A5MyEeYbRk2PmozvNJ5hzYYZHU8IUQnk5Nt/F4rNFfCFBxcy+MfB1POux2f9P2NQo0H4e/obHUsIUclIC7ySWX5kOcMWDKN1YGs2PrIRT1dPoyMJISopR2iB28xJzPScdO6Zdw8h1UNY9eAqKd5CiOtyhBa4TRRwrTVjV4wlMy+T5zo8h6+7r9GRhBCVnLTAK4m0nDS+2PUFjWo2YmTrkUbHEULYAEcYRmgTBXz98fUAPNn2Sbv/AxFClA/pQqkkdiTsAKBPWB+DkwghbIV0oVQSNTxqAI5xk1IhRPnIMefg6uRqdIwKZRMFPDIoEvirK0UIIW4k35KPi8nF6BgVyiYKeMegjrQKbMWMHTPIM+cZHUcIYSPsfVbSUhdwpZSTUmqXUurXwte+SqmVSqnDhY8+FRVSKcW4TuPYn7KfJYeWVNRuhBB2xN7nAoeytcBHAweueD0BWK21bgisLnxdYS6P/f48+nPyLfkVuSshhJ2w93tilqqAK6WCgH7AF1csHgjMLnw+GxhUrsmucUeDOxjfaTwrjq6g/w/9SctOq8jdCSFsnCMMeihtC/wDYDxguWJZgNY6EaDwsdgZpZRSo5RSUUqpqJSUlJsPqky83ettZt01i9Vxq+n8VWeOph696e0JIYStu2EBV0r1B5K11tE3swOt9eda63Za63Z+fn43s4mrPNbmMZaPWM6p9FM0/rgxzyx9RrpUhBDFkpOY0BkYoJQ6DswFeiil5gBJSqlaAIWPyRWW8ho9Q3uy8l8rybPkMTNqJltPbrXWroUQNkJOYgJa65e11kFa6xBgGLBGa/0AsBh4qPBtDwGLKizlNXLycxi6YChOyolXb32VjnU7WmvXQggbIicxS/YW0EspdRjoVfjaKvIseSRdSqJhjYYMbDQQJ+VkrV0LIWyEnMS8htZ6nda6f+Hzc1rrnlrrhoWPqRUT8e+qulZl1l2zOJJ6hFtm3UKDGQ14aeVL7ErcZa0IQggbIH3gldT9ze8n6cUkvhzwJRE1I3h/2/u0+bwNjy561CH6voQQwmYLOBRc3DOy9UiWjVhG0otJhNcI5+uYrzmbedboaEIIgzlCQ86m7ol5Pb7uvvh7+mPRFvw8//lwRSGE7ZOTmDYkz5xX1C/+yc5PuJB9wehIQgiDyElMG7NsxDKm95lOrjmXp397mlrv1eKBnx9g0cFF5OTnGB1PCGFlchLThvi4+/Bch+eIeSKG6FHRjGw1kl8P/cqgHwcRNC2ISWsncS7znNExhRBW4Ah94HZVwC9TStGmVhs+7vcxyeOSWTZiGZ3qdmLKhimEfxTO/637P1YdW8X5rPNGRxVCVCB77wO3m5OYJXF1cqVPWB/6hPVhT9Iexq0cx+vrXy9a38SvCe/c/g79wvsZmFIIIcrOLlvgJWkR0IIVD6wgdXwqq/61ird6voVFWxj20zBSLt38TIlCCGEEu2+BF8fH3YeeoT2LfiK/iCT8o3Ca+TcjwDOAwKqBdKrbieHNhtv9r2BC2DN77wd3qBZ4cdrVbsfOx3cyIGIAziZn9qfsZ86eOYz4eQQPLXxI+smFsFFKKbsfSuiQLfBrta7VmtmDZhe9tmgLU9ZPYfKGySw8uJCdj+8komaEgQmFEGWlUNICd0QmZeK1bq+x5sE1XMy9yOAfBzNv3zy5cYQQNsQRWuBSwK+ja0hX5t87nzxLHkMXDCXw3UAe/OVBPo/+nL3Je+3+f3chbJkjtMClC+UGhjQZwuBGg1l6eCkL9i/gt8O/8d2e7wDwdPGkUc1GjGw9khHNR+BdxdvgtEKIyxyhBS4FvBScTE4MiBjAgIgBaK2JuxDH+uPriTkTw8b4jTzz2zP8+7d/06ZWGwY1GsTAiIE0828mI1iEMJC0wMXfKKUI9Qkl1CcUALPFzLrj69h8cjPLjixj4tqJTFw7kVCfUIY2HcqotqMIqR5ibGghHMD5rPP8euhXMvMy+e3IbyRcTDA6UoVT1vwfql27djoqKspq+zNC4sVElhxawi8Hf+H3o7+jtaZrSFd6hfbisTaP4e/pb3REIexKalYqM3fO5O3Nb5ORmwEUdG+2q92Op295mvua3mdwwn9OKRWttW73t+VSwCtOfFo8s6JnseTQEnYn7cbTxZMXOr7A2I5jpb9ciHLS6tNW7E7aDcDqB1cTXiMcX3dfPFw8DE5Wfkoq4DIKpQIFewczpccUYp6M4eAzB+nbsC9TNkyh/vT6DF0wlF8P/Wp0RCFs3tiOY4ueZ+dnk5mXaVfF+3qkBW5l0aejmbZtGiuOruBc5jk2PLKBLsFdjI4lhE2L+CiCQ+cOFb3Wr9nXyUtpgVcSbWu3Zc7dc4gbHYenqydvbnrT6EhC2Lw/n/qT5yOfB6CBTwOD01iPjEIxSFXXqgyIGMDi2MWczzqPj7uP0ZGEsDnHzh9jyLwh7E/ZT4654K5bn/X/zOBU1iMtcAM91voxMnIzpC9ciDLKt+TT67teNJjRgF1ndpFjzmHO4DmkT0inZ2hPo+NZjbTADdQ5uDPhNcJ54tcnyM7P5vG2jxsdSQib8OPeH1l1bFXR60v/ueQwJy6vJC1wA7k6ubL6wdUEewfzxK9PcOz8MaMjCWETGtVsdNXrhh82dMj73d6wgCulqiildiildiul9imlXi9c3lIptVUp9adSaolSqlrFx7Uv+ZZ8vt71NbHnYnF3ccdJORkdSQib0KZWG74a8BXjO40H4PTF09ScWpN+P/TjQMoBg9NZzw2HEaqCCT08tdYZSikXYBMwGvgQeFFrvV4pNRKor7WeeL1tyTDCqy2OXczAuQMBiB8TT13vugYnEsL2pGalsv74enae3lk0qkuGERbSBTIKX7oU/mggAthQuHwlcE85ZXUYvRv05tbgW1Eo5u+fb/cT7whREXzdfRnceDCv3vZq0TKLthiYyHpK1QeulHJSSsUAycBKrfV2YC8woPAt9wLFNh+VUqOUUlFKqaiUFLlx8JXcnN1Y8cAK7m58N2N/H8uC/Qtuajt55jzMFnM5pxPCtrg6uRY99/ivB2vj1hbNjWKvynQlplKqOvAL8CyQD8wAagCLgee01jWu93npQime2WKm+tvVuT30dn6+7+dST0N7JPUIIxeNZFP8JjQakzLh6uSKv6c/rQJb0TqwNa0CW9G2VlvpnhF2T2tNgxkNiLsQd9XymX1n8tQtTxmUqnyU22RWSqnXgEta63evWBYOzNFat7/eZ6WAl2zK+ilMWjeJ0R1G89btb1HFucp13x+fFk/32d25kH2BJ9o+gbuzO7nmXHLMOZxKP0XMmRhiz8UW/SoZ7B1Mx6COBHgGUMW5Cm7ObgRWDaRNrTa0DGiJu4u7Nb6mEFZxLvMcn0R9wsS1BaflbL1PvKQCfsNx4EopPyBPa31BKeUO3A68rZTy11onK6VMwKvAp+We2oG8eturxCTFMH37dBYeXMiU7lPoHdb7qulnLdrCmrg1zNkzh7l75wKwZPgSejXoVew2M/My+TPpT3Yk7GBj/Ea2J2wnNSuVnPwccs25RXcrcXNyY3DjwfRr2I+e9XtSy6tWxX9hISrQH4l/FBXv7iHdDU5TcUozCqUFMBtwoqDPfJ7WerJSajTwTOHbfgZe1jfYmLTAr09rzapjqxizYgz7U/YD0Ny/OW1qtSHXnMum+E2cTD+Jt5s39za5l0ldJ91014hFW0hITyA6MZpVx1Yxd+9czmUVjKNtXLMxPev3pGdoT7qFdKN6lerl9RWFqHBZeVl4vPHXRT1rHlxD9/q2XcRlPnAbYraYiU6MZk3cGtbErWFfyj7cnd0JrxHOgy0fZFCjQTfsYikri7YQcyaG1cdWszpuNRvjN5KZl4lJmRjVZhSf9P+kXPcnREXZcnILnb/qDMCdYXfy24jfDE70z0kBF2WSa85lc/xmenzbg5oeNUkZJyOIhO1Yemgp/f/XH4AF9y7gnia2PcpZppMVpaK1ZuOJjYxZPoahC4YCFE3TKYSt6Bfej2UjlgEwZP4QxiwfY2ygCiIFXBQ5ffE09y24j9u+uY1vYr6hR/0eLBy6kAldJhgdTYgy6xPWh34N+wEwfft08sx5BicqfzIboSA9J50nfn2C+fvmY9ZmJt42kfGdx1PVtarR0YT4R55t/yxLDy8FwNlkf+XO/r6RKLNfDvzC3L1ziQyK5LvB3xHmG2Z0JCH+sUu5l3hw4YMAvHP7O6W+QM6WSAEXdA3pSmDVQHYk7GBJ7BKe7yh93sK25OTnMH//fHaf2c2JtBMkZiTyZ9KfpOWkMWfwHEa0GGF0xAoho1AEAGnZaTy86GEWHlxI7L9jCa8RbnQkIUplw4kNjF85nu0J23FzcqNe9XrUqlqL+j71eaD5A3Zxh56bvhJTOAbvKt483LKggO9P2S8FXNgErTVdv+kKwNReU3mh4wuYlOOMzZACLjh07hBf7/qamVEzCakewh0N7jA6khA3lJWXRctPWxa9HtV2lEMVb5AC7tBSs1J5ZfUrfBb9GUopBkQMYFrvaQ55b0FhW05fPE2Tj5uQlpNWtMxR5gC/khRwB7UjYQdD5g3h9MXTPNfhOV7q/JJMYiVsQtz5OEJnhAIQUSOCmCdjyn1qCVshBdwBnb54mju+uwMfdx+2PbaNdrX/dm5EiErJoi1Enf5rIEQTvyYOW7xBCrhDGrdyHNn52az810oZ8y0qtf0p+/lg2wekZqWSkplCzJkY0nPSi9aH+oQamM54UsAdzK7EXfzw5w/8p8t/pHiLSi0rL4uOX3YsKtiRQZE80PwBbq13K4MbDcbN2c3ghMaTAu4gLuVeYs6eOby1+S283bwZ33m80ZGEuC43ZzcGRAxgzp45AKx4YAXV3KoZnKpykQJu57Lzs5mxfQbvbH6Hc1nnaFOrDV8N+ArvKt5GRxPiunaf2U3s2VgAFg9bLMW7GFLA7Vh2fjY9v+3JlpNbuDPsTl7u8jJdgrvY5ZwQwj5En45mcexitp7ayqpjq/Dz9GP2oNncFXGX0dEqJSngdirfks/gHwez5eQWfrj7B4Y3H250JCFKpLXm/234f/zf+v8DCm7r98qtrzC201i5pd91SAG3UzsTdrL8yHKm9poqxVtUGgnpCRw9f5SIGhHkWfKYuHYiTsqp4B6v6yYxpMkQZt01S4p2KUkBt1PJl5IB6Fqvq8FJhCi4Rd9ra1/j7c1voymYQC/YO5j4tHgAFh5cCMATbZ+Q4l0GjjVxgANp4NsAgJgzMcYGEQ7tvS3v0eazNvT/oT9vbX6rqHhfNrnbZKo4V+F89nkeaPGANDjKSKaTtVNaaxrMaICXmxdrH1qLr7uv0ZGEA+r2TTfWn1hf9Pqz/p/RJbgLKZdS6BLcBSeTE2aLmez8bDxdPQ1MWrnJTY0djFKKab2ncfDsQTp80YHlR5aTlZdldCzhYFoGFMwWWL96fTY9solRbUfRxK8JXUO64mRyAsDJ5CTF+yZJC9zObY7fzP0/3098WjxOyokWAS0Y3Ggwz3V4TsaCiwp34sIJhv80nK2ntmJSJrxcvXA2OfPqba8yJnKM0fFsRkktcCngDiArL4sVR1cQfTqadSfWsTl+M4FVA/mo70fc3fhuo+MJO5eVl8WM7TN4b+t7pGSmANDcvzl7ntpjcDLbIQVcFIk6HcXjSx4n5kwM/cP782z7Z7k99HaHmwxfVJysvCyWHVnGucxzTN4wmVPpp4rWuZhcSBybSA2PGgYmtC1SwMVV8i35vLnxTWbsmMHZzLOE+YbxQPMH6B3WG5MyEeAZQLB3sFy1KW7Ko4se5auYr65aFuoTyrZHt+Hn6WdQKtt10ycxlVJVlFI7lFK7lVL7lFKvFy5vpZTappSKUUpFKaXaV0RwUTGcTc5M7DqRU8+f4vu7v6e2V21eX/86Hb/sSIcvOhAyPYTA9wKZu3eu0VGFDckz5/Hj3h9Jzkz+27ruId2leJezG7bAVUETzFNrnaGUcgE2AaOBycA0rfUypVRfYLzWutv1tiUt8MrtbOZZ1h1fV/ArbkYir69/nbTsNEa2Hsk9je+hhkcNPF08CfUJlZa5KNanUZ/y1NKnrlrWrnY7BkUMYlzncbg6uRqUzLbd9F3pdUGFzyh86VL4owt/Lk8P5g2cLp+owig1PWoypMmQotfdQrrx6ppX+XLXl3y88+Oi5c38m/Fwy4cZ1XYUXm5eRkQVlVS/hv3w8/ArOlm57+l9NPFrYnAq+1WqPnCllBMQDYQBH2utX1JKNQZWAIqCrphOWusTxXx2FDAKIDg4uO2JE397i6jkLuZcZP2J9eTk55BwMYG5e+ey9dRWfN19ebHji0QGRZKWk8ap9FOczzpPZFAkPUN7yklRB3Ug5QBNZjYhzDeMQ/8+JL+tlYNyOYmplKoO/AI8S0FRXq+1/kkpdR8wSmt9+/U+L10o9mNnwk4mrZvE8iPLi13/cd+PefqWp62cShgpKSOJU+mnuGPOHaRmpeLn4Ufi2MSiC3bEzbvpLpQraa0vKKXWAX2AhyjoCweYD3zxT0MK23FLnVtYNmIZx84fY8H+Bby06qWidZFBkQyIGHDV+89mniUzL5Ng72BrRxUV7NC5Q8Sdj+PO7++8aq6TBfctkOJdwUozCsWvsOWNUsoduB04SEGf9+WZZ3oAhysoo6jEQn1CeaTVI3i4eADwy9BfWDZiGXW86nAx5yJLYpfQ9Zuu+E31o94H9Qj/MJxnlj7DxhMbDU4uysPhc4eJ+CiCPt/3+dtEVa+vf92gVI6jNC3wWsDswn5wEzBPa/2rUuoCMF0p5QxkU9jPLRyPn6cf+57eR89vezL4x8EAuDq5kmfOQ6Op41WHKd2n4OXqxYqjK5gZNZOZUTP56M6PqO9Tn/rV6xNYNRAfdx+Dv4koq1CfUAZEDGBx7GIAqlepTqhPKKfSTxFRI8LgdPZPLuQR5SYnP4c1cWvYl7KPlEspeLh4EBkUSff63a8aPrY5fjNdvu5y1WddnVwZ1mwYz9zyDLfUvkVOfNmQI6lHeGjhQ2w5uaVo2T2N72HevfPkRHY5kSsxRaWSb8knKSOJuAtxnEw7ycb4jXy7+1su5V0iwDOAQY0GMSZyDI1qNjI6qriB27+9ndVxq/+2POuVLKo4VzEgkf2RAi4qvbTsNH4+8DMrj63kf3v/B8CmRzbRObizwcnE9USdjuLF31+8at5vX3dfzo0/Z2Aq+yIFXNiU19a+xuQNk+kY1JF2tduRnZ/N/c3vp1tIN6OjiWJordl6aiuPLX6M5EvJ7H5yN3Wq1TE6lt0ol2GEQljLvU3vZe6+uexJ2sO+lH2k56Qz649Z9A/vz8hWI2kZ2JKQ6iHSx2qghPQEVset5rfDv7E6bjVnM88S4BnA3CFzpXhbibTAhU3IzMvk4x0fM3nDZDJyC2Z2cHVypY5XHXrW78nUO6bKzXCtJCc/h7c3v82UDVPIt+Tj5+FHv/B+RNaJZHjz4VRzq3bjjYgykS4UYRcy8zLZm7yX3Wd2cyT1CL8f+52YMzHMumsWj7V5zOh4du9s5lm6fdONfSn7GNF8BOM7j6eZfzP5TaiCSReKsAseLh60r9Oe9nUKZi/uergr/X7oh9liNjiZY1gSu4R9Kfv4vP/nPN72caPjODz5b1PYtJYBLfFw8eDJpU/yr1/+xcm0k0ZHsmu31LkFkzIxd99ccvJzjI7j8KSAC5t2JPUIDX0bAjBnzxyGLhhqcCL71sy/GS91fok1cWv4z+r/yG8+BpMCLmzaF7u+YHfSbroEd6FVYCseavmQ0ZHs3mtdX6N9nfa8v+19QmeE8ubGNzmbedboWA5JCriwaS38WwBwZ9id/DHqD55o94TBieyfm7Mbm0duZsG9CwjzDeM/a/5D3Wl1GTJvCIkXE42O51DkJKawac91eI6YpBheWfMKZzLOMK33NJnC1AqcTc7c0+Qe7mlyD/uS9zFzZ8EEZV5uXnw98Guj4zkMKeDCprk5u/Hd4O8I8Axg2rZpHE49zLu93qWpf1Ojo9k0s8VMalYqSZeSSL6UTFJGwWPypeSiZdn52eSYc8g153Ius+Cy+TCfMIOTOxYp4MLmmZSJ93u/TwOfBkxYPYFmnzRjUKNBfNb/M/w9/Y2OV2lorcnKzyqau/2yMxln+H7P96w4uoIzGWdIvpRMSmYKFm352zaclBP+nv74e/rj7uKOm5MbXq5eeLl68VrX1xjefLi1vo5ALuQRduZc5jlm7pzJG5veoKlfUzaN3OTQM+L9mfQnq+NWs+74OjbGbyQ1K5Vg72AGhA+gU91OzN8/n8WxizFrMy0CWlC/en0CPAOKinRA1Sueewbg4+4jF+0YQK7EFA5lcexiBs4dyJs932RClwlGxzHEuN/H8e7Wd4GCGy90rdeV+tXrE5MUw9JDS8kx5+Dr7stjrR/j4VYP09ivscGJRUnkSkzhELTWpOWkFd1A4truAkdxJPUI7259l3+1+Bdv9HyDoGpBV60/m3mWuPNxtAhogZuzm0EpxT8lBVzYvLTsNGbunMm3e74l7nwcOeaCKwR93X0Z3szx+mSz8rJ4eunTmJSp2OINUNOjJjU9ahqQTpQnKeDCpqXnpNPi0xbEp8XTPaQ7d4XfRa2qtQioGkCP+j3w8/QzOqJVXcy5SL8f+rEpfhOf9f+s2OIt7IcUcGHT5u6dS3xaPLcG38qKB1bg4uRidCRDfRr1KRvjNzK6w2iZbMoByOlkYdOGNBnC3Y3vZmP8Rmq8UwPT6yYGzR2ENU/OX+vK4Xf5lnyr7ruBbwMApm+fzvRt04sdCijsh4xCEXZh4cGFrDq2io93fkw973ocH3Pcavs+fuE4q4+tZtPJTWyO38zh1MM4m5yvKt4dgzoS6hNKqE8oLQJa0KZWG+pXr49SqtzzzIqexed/fE7U6Sh61u/JmMgx3Bl2p1yhasNkGKGwe1prwj8KJzMvk3UPraNhjYYVur/tp7bzxqY3WBy7GIAa7jXoHNyZlgEtybfkk3wpmS93fUmX4C64Orly7Pwx4tPii1rF3m7ehNcIp2GNhvRu0JuBEQPxruJdLtm01szcOZPJGyaTfCmZgRED+em+n6SI2ygp4MIh7EzYSd8f+mLRFlb+ayVtarUp933EnIlh7O9jWRO3Bl93X55t/yxDmw6lUc1GN2xR5+TnsDd5L38k/sGuM7s4knqEvcl7ScxIxNXJlT5hfRjSeAi9GvQisGrgP86aZ87j/a3vM2H1BJ6PfJ73e7//j7cprE8KuHAYR1OP0m12Ny5kX+D1bq/zcKuH8XX3LZdtrzu+jvvm34dGM6HzBJ5o9wRVXav+o21qrdl2ahvz989n3r55JFxMuGq9v6c/3UO608y/Gc38m1HPux6N/RqX+gpTrTVV/luFPHMe6S+n/+O8wvqkgAuHcjLtJCMXj2TVsVW4ObnxSKtHmHrH1JsqXhdzLjJ1y1TWHl/LpvhNNPRtyOLhi2lUs1G557ZoCzFnYlgTt4ZD5w6xKHYRrQJbcejcIY5fOF70PheTC70a9OKRVo8wuNHg63aNTFo7iSkbpjC522Qmdp1Y7plFxZMCLhxSzJkYPo36lM+iP+O59s8x/c7pQEGr9LfDv5Gdn001t2poNHW86vxtFsOFBxcy+MfBALg5ufFa19d4rsNzeLp6Wv27ZORmsD9lPycunChqsZ9ML7iF3MCIgfRt2Je61eri7uJORm4GF7IvsO3UNj7e+TEKhXmSuUJOmoqKd9MFXClVBdgAuFEwbnyB1vo1pdSPQETh26oDF7TWra63LSngwgjLjyznzu/v5KGWD/H1wK9RSrHy6ErumHPHVe9zMbnwZs83ebLdk3i6erL00FL6/68/4TXCeaTVI4zvPL5STeRktphZsH8Bw34aVuJ7XJ1cGdp0KO/0eqdc+tSFMf5JAVeAp9Y6QynlAmwCRmutt13xnveANK315OttSwq4MMLWk1u59etbMWsztarWok2tNtTzrsen0Z8WO07aw8WDQY0GcfriadYfX0/yuORKf9m5RVs4mXaSxIxEMvMy8XL1oppbNep613XY+WDsSbl0oSilPCgo4E9prbcXLlNAPNBDa334ep+XAi6MkpCewMKDC9mWsI1dibvYl7IPhSKoWhBpOWmk56TzRNsnGN5sOP/b+z9m755Ndn42JmXCw8WDznU7M77zeHrU72H0VxEO6B8VcKWUExANhAEfa61fumLdbcD7xW28cP0oYBRAcHBw2xMnTtzcNxCiHMWdj2P27tksil1E7NlYutfvzneDvysarfLO5nd4adVLf/vchZculNtYbSFKq7xa4NWBX4BntdZ7C5d9AhzRWr93o89LC1zYijxzHjN3zmR30m5qe9WmpkdNWge2pmtIV6OjCQdULvOBa60vKKXWAX2AvUopZ+BuoG25pBSiknBxcmF05GijYwhxXTc8pa6U8itseaOUcgduBw4Wrr4dOKi1PlVhCYUQQhSrNC3wWsDswn5wEzBPa/1r4bphwP8qKpwQQoiS3bCAa633AK1LWPdweQcSQghROpXnqgQhhBBlIgVcCCFslBRwIYSwUVLAhRDCRkkBF0IIG2XV6WSVUimAPVxLXxM4a3SISkSOx1/kWFxNjsfVbvZ41NNa+1270KoF3F4opaJKmvvFEcnx+Isci6vJ8bhaeR8P6UIRQggbJQVcCCFslBTwm/O50QEqGTkef5FjcTU5Hlcr1+MhfeBCCGGjpAUuhBA2Sgq4EELYKCngpaSUaqmU2qqU+lMptUQpVa1weQ2l1FqlVIZS6iOjc1pLScejcN3LSqkjSqlYpVRvI3Nai1KqlVJqm1IqRikVpZRqX7jcVSn1deFx2q2U6mZsUuu4zvFwUUrNLjweB5RSLxudtaJd51iMKFx2+ceilGpVpo1rreWnFD/ATqBr4fORwJTC555AF+BJ4COjc1aC49EE2A24AfWBo4CT0XmtcDx+B+4sfN4XWFf4/Bng68Ln/hTcW9ZkdF4Dj8f9wNzC5x7AcSDE6LxGHItr3tMcOFbWbUsLvPQigA2Fz1cC9wBorS9prTcB2UYFM0ixxwMYSME/0BytdRxwBGhvQD5r08Dl30K8gdOFz5sAqwG01snABcARLmwp6XhowLPwdozuQC6Qbv14VlXSsbjScG7i5jhluiemg9sLDAAWAfcCdY2NY7iSjkcdYNsV7ztVuMzejQFWKKXepaBrslPh8t3AQKXUXAqOUdvCxx1GhLSiMRR/PBZQ8J98IgUt8Oe11qmGJLSeMRR/LK40lILjUiZSwK+glFoFBBaz6hUKuglmKKUmAYspaDnYtZs8HqqY99vFWNUbHI+eFBSjn5RS9wFfUnDP2K+AxkAUBfMAbQHyrZO4Yt3k8WgPmIHagA+wUSm1Smt9zEqxK8RNHovLn+0AZGqt95Z5v4X9L6IMlFLhwBytdfsrlj0MtNNa/9uwYAa58nhcPimltX6zcN0K4P+01luNzFjRlFJpQHWttVZKKSBNa12tmPdtAR7TWu+3ekgrKul4KKU+BrZprb8rfN9XwHKt9Twj81akG/3dUEpNA1K01m+UddvSB15KSin/wkcT8CrwqbGJjHWd47EYGKaUclNK1QcaYv/dBVDQr9m18HkP4DCAUspDKeVZ+LwXkG/vxbtQsccDiAd6qAKeQCRw0IB81lTSsbj87+deYO7NbFi6UEpvuFLqmcLnPwNfX16hlDpOwUkKV6XUIOAOB/hHWuzx0FrvU0rNA/ZT0FXwjNbabFBGa3ocmF54ci4bGFW43J+C/k8LkAD8y6B81lbS8fiYgr8reynobvtaF9w43Z6VdCwAbgNO3WwXknShCCGEjZIuFCGEsFFSwIUQwkZJARdCCBslBVwIIWyUFHAhhLBRUsCFEMJGSQEXQggb9f8BcwUKnVBE4zIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Hispanic-heavy home districts?\n",
    "minHisp1 = 0.35\n",
    "minHisp2 = 0.40\n",
    "sumHispDistricts = 0.\n",
    "for t in range(nTracts):\n",
    "    if (HDvHisp[t] > minHisp1 and tractPop[t] > minTractPop):\n",
    "        sumHispDistricts += tractPop[t]/avgDistrictPop\n",
    "        if (HDvHisp[t] < minHisp2):\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='orange' )\n",
    "        else :            \n",
    "            plt.text(tractCPx[t],tractCPy[t],'o',color='blue', fontsize= 12)\n",
    "print(\"using a threshold of\",minHisp1,\"the number of Hisp districts should be\",round(sumHispDistricts,3))\n",
    "x,y = origMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "a92133ad-dc95-4aa1-af02-c1d71296cee6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a bar plot of seats by VAP pct Black for MI\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ALTERNATE VIEW OF BLACK VOTE: HOW MANY DISTRICTS PER 10pct BIN of PCT BLACK VOTE?\n",
    "n_bins = 10\n",
    "BlackSeats = [0.]*n_bins\n",
    "binMid = [0.]*n_bins\n",
    "for b in range(n_bins):\n",
    "    binMid[b]= float(b)/n_bins + 0.5/n_bins  #centering each bin\n",
    "for t in range(nTracts) :\n",
    "    b = int(HDvBlack[t]*n_bins)\n",
    "    BlackSeats[b] += HDweight[t]*nDistricts  #multiply by nDistricts to get number of expected seats\n",
    "\n",
    "print(\"this is a bar plot of seats by VAP pct Black for\",STATE)        \n",
    "fig, ax = plt.subplots()\n",
    "plt.bar(binMid,BlackSeats,width=0.09 )\n",
    "ax.set(xlabel=\"pct Black\", ylabel=\"number of seats\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "b1e9c437-ddf7-4202-aff0-19f338a5d013",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Black candidates would expect to win 2.685 MI seats out of 13\n",
      "Using simple Alabama-style of Blacks voting 0.9 Dem and whites voting 0.8 GOP\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see opportunity home districts?  #NEW FORMULATION based on ALABAMA\n",
    "isOppor = [0]*nTracts #will track whether this is an opportunity district\n",
    "blackFracSeats = 0.\n",
    "blackPctDem = 0.9  #fraction of Black voters who are Democrats (est'd from Chen / Stephanopoulos)\n",
    "whitePctGOP = 0.8   #fraction of White voters who are Republican  (\"  \")\n",
    "#minBlack1 = 0.30\n",
    "#minBlack2 = 0.50\n",
    "for t in range(nTracts):\n",
    "    pctWhiteDem = (1-whitePctGOP)*(1-HDvBlack[t])  #this is the fraction of voters who are white Democrats\n",
    "    pctBlackDem = blackPctDem * HDvBlack[t]  #fraction of voters who are black Democrats\n",
    "    if tractPop[t] > minTractPop and HDvGOP[t] < 0.5 :  #Dems would win this Home District\n",
    "        if pctBlackDem > pctWhiteDem : #Blacks would win the primary\n",
    "            isOppor[t] = 1\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "            blackFracSeats += HDweight[t]\n",
    "        else :  #the White Dem candidate would win the election          \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "print(\"Black candidates would expect to win\",round(blackFracSeats*nDistricts,3),STATE,\"seats out of\",nDistricts)\n",
    "print(\"Using simple Alabama-style of Blacks voting\",blackPctDem,\"Dem and whites voting\",whitePctGOP,\"GOP\")\n",
    "x,y = wholeMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "c5125128-e4bb-4337-bc5e-3f48d7cf404f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here's the MI map with Hisp+Black greater than  0.4 or even 0.5\n"
     ]
    },
    {
     "data": {
      "image/png": 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llLtj/5/KIG6XZdM2ArwDaObfjCWjl7B98nb2PbSPe9vcy6K9i9hyakue8t/v/x6A7iHd8z2eh9mDrbFbMb9oRr2geHPTm0xaOgmwD/Gb7fjF44xoNoLjU47j6+HLjF9mcPjCYd666a08T7cKUVyPdHmEAQ0H8OQvTxL4ZiBjF4/lcsZlwD7z1NzIucxcP5PkjOQijiRKqzT96F9XSrXD3hQTBTwAoJS6Dns3ynCtdZZS6l/AKuzdKz/TWu8rXciu7YudXxCXHMdbN73F8OuH56wf3248X+z8ApMyobUmIiaCLFsWSelJAPQN68uULlMAsJgtdA3uSh2vOmTZsrjjf3ew9NBSAKavmU41t2rMv3V+nqdSUzNTqelZE4vJwqSfJjFvxzz+dcO/6N+wvxM/vXAlFrOF1feuZsupLXyx8ws+jvyYr3Z/Rfu67anuUZ31J9YD9klj7mlzj8HRujYZAqGCmbx0Mp/u+JQRzUZwNOEooX6h3Nz4Zpr7N6fP/D40rtUYfy9/ImIi8uznZnIj9enUnIeYcnt94+s8+UveIX0b12rM4YcPA/abZUFvBTG5w2RGtxpNt8+6cVeru5h/63wsZku5fVZRtfx85Gd+O/4bPx36iQPnD+Ssb1+3Pdsf2G5gZK5BhkCoRNrVbUd9v/qsPLISrTU7z+zkp4M/8WT3JwnxDSEhNQGrzXrVfqNbjc43yQP8cuyXq9blbnf/Zs83pGWl0bxOc8K/CSfUL5Q3Br4hSV6UqcGNBzO48WBeG/gaVpuVIxeOMHHpRP44+QenEk/l3LwVZU9q9BVc7kHE9HP2f6szl88Q9FbefvIREyLoEnx1z9X1J9bT+4u/u6c91/s5bm9xO01rN+V4wnEW7l3I25vfRimFl8WLam7V+G3cbzLYmHCK2RGzeXTVo9SrXo+HbniIcW3HScK/RoXV6OV54gou2Dc4Zzn7S7muT12+v/P7POUKekL10VWP4u/lT4ivvfPTuLbjqF2tNqO+G0WzD5vxwu8vkJSRRGJ6IvEp8fw4+kdJ8sJppnadyvbJ2zmVdIqnf32a0d+PNjoklySJvoJrUacFFpO9CWXO1jk560c2H8nHQz6mS70uRE2JolGtRvnu725253zKeaITo1kyegkNajbg/377P1YeXklz/+Y55ToEdeCb276hTWCb8v1AQlyhfVB7/pz4JwBtAuT3rzxIG305eWvTWxy5cOSq9dldFZXjWbL83l+5Ldg3mOMXj7PvXN4OSw90eiDnKdb82LQtZwCyfg36MazpME5eOsmivYu4vcXtRMREUNOzJn/c/wct6rQozccVolQ6BHXAzeSGn6ef0aG4JEn05WBf3D4eX/M4YJ8TM1t200v2QE75vc9vW2pmKgA9QwufX/VKpxJP5UzXl90f/uGVD5OcmUxdn7ocTThK64DWkuSF4dxMboT6hV41aqUoG5LoSyElM4WfDv7E9tPbCfYNpl71emyL3ca8HfPw8/Bj/z/3GzpjTohfCCemnsDD7EGgTyBAzlAKb21+iwDvAGYPnm1YfEJkO510mmMJxxjSZIjRobgkSfTFlJCawP7z+zlw/gAHzh9gT9we1p9YT0pmCm4mN7JsWQCYlZmbGt3ErH6zKsS0aKF+eQeI+3Pin5y9fJYMawZ1vOvgZfEyKDIh/ubr4UuQTxCf7fiMW5vdSr8G/YwOyaVIoi9CYnoiU3+eyuc7P89Z5252p2ntptzX9j7uaHkHver3Ii45juhL0TSv0xwfdx8DIy6cSZkqxBeQELl5u3uzZdIWQt4J4a3Nb0miL2OS6Ivw3G/P8eWuL3m066P0b9CfZv7NCKsRlmfMdrB3eazrU9egKIWo/M5cPgPAoEaFTz4vSk4SfRHWHFvDwEYDeXvQ20UXFkJcs4+2foSXxYtxbccZHYrLkX70RfDz9ONQ/CHSstKMDkUIl6S15qOtHzF/13zGthkrXSzLgST6IrzQ5wWOJRzjlQ2vGB2KEC5pdsRsHlrxEAMaDuC1ga8ZHY5LkkRfhAENB3B367t5deOrHE84bnQ4QriUDGsGL/z+AuFNwllxz4o8k+qIsiOJvhheG/AaWbasPD1vhBClt+P0Di6lX2JMmzE5M6CJsidXthiCfYNpUacFkaevnDZXCFEadbzt80NnP/0tykexE71SyqyU2qGUWnbF+seVUlople/wiUqpKKXUHqXUTqVUpRx7+FD8IfbF7aN1QGujQxHCpWSPqnrlRDqibJWke+UUYD+Q04imlAoBBgIni9i3r9b6fMnDqxi+2fMNGs1NjW4yOhQhXEr22Daebp4GR+LailWjV0oFA0OAeVdsegd4Aqh4s5eUoehL0QBsit5kcCRCuJaXN7wMwFM9njI4EtdW3Kab2dgTui17hVJqOHBKa72riH01sFopFamUmlxQIaXUZKXUNqXUtnPnzhUzLOeISYrB3ezOfe3uMzoUIVxKTc+aAPJUeTkrMtErpYYCcVrryFzrvICngWeLcY7uWusOwM3AP5VSvfIrpLWeq7XupLXuVKdOneJF7wSJ6Yn8cuwXHun8SJ7ZnoQQpXcw/iCtAlrlzL0gykdxavTdgeFKqShgEdAP+ApoAOxyrA8Gtiulrvpa1lrHOn7GAYuBzmUSuZMcjj+MTdtoW7dt0YWFEMWmtWZT9Cb6hckAZuWtyESvtZ6htQ7WWocBo4Fftda3aa0DtNZhjvUxQAet9Znc+yqlvJVS1bOXgZuAvWX9IcpTy4CWeFu8pX1eiDKWlJFEalZqzlwJovyUeT96pdR1SqkVjreBwB9KqV3AFmC51vrnsj5neUrNTCXdmk519+pGhyKES8n+P7Xs0LIiSorSKtHolVrrdcC6fNaH5VqOBcIdy8eASt3m8dmOz8iyZXFX67uMDkUIl3I2+SyA3PtyAnkytggrj6ykdUBr2tVtZ3QoQriUj7Z+hEIxq98so0NxeZLoC/H9X9+z9vhawpuEGx2KEC7DarOSZcti59md1POtR5PaTYwOyeXJxCP5OHD+AC/+/iIL9y6kc73O/F+v/zM6JCEqvYTUBGasncG87fOwaisAQT4yraUzSKK/QnxKPC3ntMSmbTzV/Sme6/OcPJ4tRCnYtI1v9nzDY6sfIz4lnvHtxlPPtx5J6UlM6DDB6PCqBEn0uVhtVu754R7cTG78MuYXetbvaXRIQlQaF9Musi9uH0kZSbib3Wkb2BY/Tz+GLxzOyiMrueG6G1h17yq532UASfQOF9Mu8uiqR1l1dBWfDP1EkrwQxXT28llmrp/JJ5GfkGnLzFnvZnJjWNNhrDyykrdueoupXafKmPMGkavuMOGnCXyx8wumdpnKpA6TjA5HiEph1ZFVNHqvER9t+4jx7caz/O7lDGs6DIAsWxaLDyzm5X4vM+3GaZLkDSQ1emD5oeX8sP8Hnuv9HM/3ed7ocISoFJLSk7j/p/sJqxHG4lGLc3rPhPqFYjaZqVe9HhM7TJSmmgqgSid6rTVzts5h2upptK/bnundphsdkhCVxq/HfyU2KZb5t87P00WyVUArFo9abGBk4kpVNtEnpScxfsl4vt//PeFNwvny1i/xdvc2OiwhKo31J9bjZnLjxuAbjQ5FFKFKJvrkjGSGfDOEDSc38ES3J3hlwCvSfihEMWmtefH3F/l0x6cMv364VJAqgSqX6BPTExm2cBgbozey8LaFjG412uiQhKhUTl46yfO/P09Nz5o83/t5o8MRxVBlqrE2bWPZoWX0+rwXm6I38c3IbyTJC3ENLqReAOA/w/5D68DWBkcjisPlE31qZipzI+fSck5Lhi0cRnxqPD+O+pFRrUYZHZoQlVJ0on0O5X3n9hkciSgul226SclM4c1Nb/L+lvc5n3KeDkEd+Hrk19zR4g4sZovR4QlRKf0n8j9MXmaf+vnjbR+jtaZRrUY0rNmQRjUbEeAdINMCVkAumeiT0pPoPK8zB84fYGjToTx242P0rt9bfgGFKKV6vvXw8/CjVUArTl46yQu/v4BG52z3tnjTsGZDGtdqzODGgxndajS+Hr4GRiygBIleKWUGtgGntNZDc61/HHgDqKO1Pp/PfoOBdwEzME9r/Wqpoy7CmmNrOHD+AO8Nfo+Huzxc3qcTwiXti9vH8sPL2Rq7lQupFzApEw1rNOQ/w/7DHS3vACAtK40TF09wNOEoRy8c5VjCMY4mHGX32d0sPrCYR1c9yp0t7+TFPi8S4hdi8CequkpSo58C7Adyvp6VUiHAQOBkfjs4vhw+dJSJAbYqpX7SWv91zREXQ+d6nanmVo2Ptn3EbS1u47rq15Xn6YRwOTGJMbT6qBUAjWs1JtA7kCxbFt/t/4652+cS4RdBl+AueLp5cr3/9Vzvf32e/bXWbI3dyoy1M/hi5xekZKbw39v/a8RHERTzZqxSKhgYAsy7YtM7wBOQ62+3vDoDR7TWx7TWGcAi4JZrjLXYgn2DWXnPSqITo+n1eS9OXDxR3qcUotLKsmURfSmac8nnOHnpJFprDscfztl++OHD/HH/H0RMjCBqShS+Hr68++e7BR7Ppm2sPLKS59c9z6/HfwWgXWC78v4YohDFrdHPxp7Qc2bIVkoNx96Ms6uQtu96QHSu9zFAl/wKKqUmA5MBQkNDixlWwXqH9WbNmDUMXjCYvvP7snnCZpltXlRJsUmxvP/n+/x+4neqWarRMagjZy6fQSlFckYyG05uIC45Lqf8zY1vxmwyAzC27dg8x6ruUZ2J7Sfy3pb3OHnpJCmZKaRkppCalZqznJKZAtgnFXmhzwtM6jCJoOoywYiRikz0SqmhQJzWOlIp1cexzgt4GripqN3zWZdv7V9rPReYC9CpU6eC/kIoka7BXVk9ZjV9vujDHf+7g/Xj15fFYYWoFGzaxg/7f2DKz1M4e/ksN4bcyPGE4/x6/FeCfYMxKRPV3KrRu35vOgZ1JC0rDZu28ebmN0nJTOH62tfzSv9XrjrutBunsTtuN1pralWrhZfFCy+LF9XcquUstwpoxa3NbpUebhVEcWr03YHhSqlwwBN7G/1XQAMguzYfDGxXSnXWWp/JtW8MkPsOTDAQWxaBF1fnep2Z2W8mj61+jMjYSDpe19GZpxfCMC/9/hLP//48oX6hbJ20lfZB7bFpG2lZaXhZvArcb2rXqVxIvUDDmg3z7alWz7cea8asKc/QRRkrso1eaz1Dax2stQ4DRgO/aq1v01oHaK3DHOtjgA5XJHmArUATpVQDpZS7Y/+fyvYjFG1C+wn4uPvw0baPnH1qIZwuNTOV+Tvn8+rGVwlvEs7hhw/TPqg9ACZlKjTJA9SsVpNGtRpJd2QXUub96JVS12HvRhmutc5SSv0LWIW9e+VnWmunP07n5+lH/wb9+ePkH84+tRBON+q7USw9tBSAecPm4W52NzgiYbQSDYGgtV6Xuw99rvVh2X3otdaxWuvwXNtWaK2baq0baa1nlT7kaxPgHUBcchxZtiyjQhDCKZr7NwfsSV5uggqoAmPdZLu58c0kpCWw8vBKo0MRolwlpifiYfbgrtZ3GR2KqCCqTKIf2nQo9f3q8+L6F40ORYhydSThCOnW9JxRJoWoMonezeRGdY/qbIvdRkJqgtHhCFFuNkVvomNQR3kiXOSoMol+U/Qm9sbtRaFkRhzhss4lnyMlMwUvi5fMmiZyVJnfhHk77KM3xE2Pk14IwmVtit4EwGM3PmZwJKIiqRKJ/n/7/scXO79gerfp+Hv5Gx2OEOVixeEVTF8zHYB2ddsZG4yoUFxyPPrc5mydwyMrH6FrcFde6vuS0eEIUS5WHF7BkG+GADAnfA71a9Q3OCJRkbhsoj97+Syjvx/Nuqh1BHoHsmbMGjzcPIwOS4hy0TW4K+5mdzKsGfQJ62N0OKKCcbmmG601cyPn0uzDZmw8uZH72t3Hiakn8HH3MTo0IcpNrWq1iJoShb+XP3f/cDfpWelGhyQqEJdK9FprHlv9GA8se4D2dduz+x+7+fyWz6UmL6qEoOpBfDr8U3ae2cmYxWM4cznv0FMX0y5yPuWqSeBK5GLaRfaf21+qYwjnc6mmm08iP+GdiHd4pPMjzB48WwZlElXO8OuHM6vfLJ5b9xxLDi7hpkY30b9Bf7TWTFs9DYCLT17Ez9OvRMfVWrNg9wLG/mgfnz7t6TSpQFUiSusyGfq9THXq1Elv27atxPu1+7gd7mZ3/pz4pyR5UaUdjj/MnK1z+H7/90QnRufZ5ufhR9fgrnQL6UbP0J50Ce5S6IiWURejeGDZA6w+ujpnXfwT8dSqVqvc4hclp5SK1Fp3ym+bS9XojyUcY3y78ZLkRZXXpHYT3hn8Dm8Pepv41HgupF7A2+LN6qOriYiJYHPMZp5f9zwajcVk4YZ6N9ArtBd9G/Slf4P+mE1mEtMT+Xjbx7z4+4sopfjg5g8wm8z8Y/k/SM1MhWpGf0pRXC6V6BvXaszvJ35Hay3JXghAKYW/l3/O8yPj249nfPvxgL29fVP0JtafWM/6E+t5c/ObvLrxVaq7V6euT11OXDpBhjWDoU2H8mH4h4T6hfLVrq8ASM1KNewziZJzqUT/0A0PMWnpJP731/+4s+WdRocjRIVWw7MG4U3CCW9iH1U8JTOFFYdXsP7Ees4mn+WW62/h9ha30yX472meq1ns1fjUTEn0lYlLtdFnWDPo/UVv9pzdQ8TECFoFtCqH6ISoupYdWsawhcPoG9aX2l61MSszbiY32gS24ecjP2PVVmzaRveQ7rw64FWjw61SCmujL3aiV0qZgW3AKa31UKXUS8AtgA2IA+7TWl81H6xSKgpIAqxAVkGB5HatiR7sM953nNsRhWLt2LU0r9P8mo4jhLhaTGIMo78bTWJ6IlZtJcuWxaH4Qznbe9fvzdGEoyRnJHPhSRkm2ZnKKtFPAzoBvo5E76u1TnRsewRoobV+MJ/9ooBO2TNQFUdpEj3A3ri9DPhyADZtY/WY1TLuhxDlaNmhZew/t5+mtZtyS7NbePa3Z5m5fiaTOkzCpm3YtI3GtRozo+eMMj1vdu6S+3F2pU70SqlgYD4wC5h25XSCSqkZQKjW+h/57BuFkxM92LuX9f+yP1ZtZdeDu2QwMyGcZOXhlUz4aQI2bcOkTJy+fBqA2tVqYzFb0Fpj0zY0usBlm7ahtS50GezPDSwZvcTIj1thlEWi/w54BagOPJ6d6JVSs4CxwCWgr9b6XD77HgcSAA18orWeW8A5JgOTAUJDQzueOHGiGB+tcDvP7KTrvK6ENwnnh1E/lPp4QoiSO3rhKG9segOrzYpSCpMyoVAFLpuUCaVUgcvZ5ZYfXs7py6c5Ne2U0R+xQihVP3ql1FAgTmsdqZTqk3ub1vpp4GlHjf5fwHP5HKK71jpWKRUArFFKHdBar7+ykOMLYC7Ya/RFxVUc7eq247nez/HvX//Nuqh1MtiTEAZoVKsRHw/9uMyPm5yZzJytc8r8uK6oOGPddAeGO5pgFgH9lFILrijzDXBbfjtn36DVWscBi4HO1xztNZjadSohviE8vvpxbNrmzFMLIcqRr4cvqVmpZNmyjA6lwiuyRq+1ngHMAHDU6B/XWt+rlGqitT7sKDYcOHDlvkopb8CktU5yLN8EOHV27mqWaszqN4uxP47l691fM6btGGeeXghRTqq7Vwfgr3N/4WXxItOaSZYti0yb42cx32s0I5qNoLZXbYM/UfkpzQNTryqlrsfevfIE8CCAUuo6YJ7WOhwIBBY77oq7Ad9orX8uXcgld0+be3hvy3vMWDuDkc1HypyxQriA7MTc9uO2pT5WXHIc/+7571Ifp6IqUaLXWq8D1jmWC2uqCXcsHwNK/69QSiZlYvag2fT4vAevbXyNF/s69Y8KIUQ5uK25PQVZbVYsZgtuJjcsJsfPErxv/mFzziVf1Y/EpbjUEAiF6R7andGtRvPaxtdoHdCaO1reYXRIQohS8Hb3ZmzbsaU+Tm2v2lxIc+2Hu6pMogf4MPxDTlw8wbgfx9EtpBv1fOsZHZIQwmC1qtUiPiWelMwUMqwZ+b4yrZkFbvOyeDG06dAK/eBWlUr0tarV4uuRX9P8w+aMXzKelfesxGwyGx2WEMJA/l7+LD+8HO+Xr/3e3ZaJW7ih3g1lGFXZqlKJHqBBzQZ8GP4hE5dO5KlfnuKNm94wOiQhhIFe7vcyvUJ74eHmgbvZPedlMVnyvHc3u2MxW/Aw/10uOjGaYQuHcTThqCT6imZChwnsOLODNze/SbBvMFO6TjE6JCGEQW6od8M1J+mGNRsCEBETwehWo8syrDLlUpODl8S7g99lZPORTF01lYV7FhodjhCiEqruYe/L/9XurwyOpHBVNtGbTWa+Hvk1vev35t7F97L04FKjQxJCVFIXUit2r50qm+gBPN08WTJ6CTZtY/nh5UaHI4SohFoHtOaW628xOoxCVelED+Dn6ceolqP4JPIT5m2fZ3Q4QohKJtAnkLPJZ40Oo1BV8mbslebfOp9L6ZeYvHQy7mb3MnkIQwhRNQR6B3L0wlGjwyiUJHrAw82DH+78gaELhzJ+yXj8vfxzJkwWQojC1PWpy/GLx5n689ScB6sybVf8zLXeYrLw6fBPqV+jvtNidKnJwUvrcsZlen3ei8MXDvPH+D9oW9fwYXqEEBXckgNLuOeHezApU57+9rn74ud+HxETwZ0t7+TLEV+WaRxlMmesMxmV6AFOJZ6iy7wuAPw58U8ZJkEIUaaeWPMEb256kz3/2EPLgJZldtzCEn2Vvxl7pXq+9Vh+93IupV9i1HejsNqsRockhHAhT3Z/Eh93H/7vt/9z2jkl0eejbd22fDTkIzZGb+SdiHeMDkcI4UJqe9VmerfpLD6wmPf/fN8p55REX4B7Wt/Drc1u5elfnyb6UrTR4QghXMhTPZ7ilutvYeqqqWw4saHcz1fsRK+UMiuldiilljnev6SU2q2U2qmUWu2YWSq//QYrpQ4qpY4opZ4qq8DLm1KKV/q/QqY1k5c3vGx0OEIIF2IxW/ji1i9oVLMRQxcO5dPtn7Jg9wL2nN1TLucrSY1+CrA/1/s3tNZttNbtgGXAs1fuoJQyAx8CNwMtgLuUUi2uPVznaubfjGk3TuPjyI9ZdWSV0eEIIVxIDc8arB27llrVajFx6UTGLB7DhJ8mlMu5ipXolVLBwBAg59FRrXViriLeQH7ddzoDR7TWx7TWGcAioGI/K3yFmf1m0qJOCyb8NIG0rDSjwxFCuJAQvxD2PbSPcW3HAfBw54fL5TzFrdHPBp7APhF4DqXULKVUNHAP+dTogXpA7gbuGMe6qyilJiultimltp07V3Hmb/R082T2oNmcSjrFGxtl7HohRNlacmAJ83fNp29YX+5tc2+5nKPIRK+UGgrEaa0jr9ymtX5aax0CfA38K7/d81mXb8d9rfVcrXUnrXWnOnXqFBWWUw1sNJCuwV1ZekhGuBRClK0mtZsA4GXxKrfpCItTo+8ODFdKRWFveumnlFpwRZlvgNvy2TcGCMn1PhiIvYY4DTew4UAiT0eSmJ5YdGEhhCiG7ae3868V9jryiGYjyu08RSZ6rfUMrXWw1joMGA38qrW+VynVJFex4cCBfHbfCjRRSjVQSrk79v+pDOJ2um4h3bBpG7vO7DI6FCGEC/h0+6d0mtuJowlHWXjbQiZ0KJ8bsVC6Qc1eVUpdj73d/gTwIICjm+U8rXW41jpLKfUvYBVgBj7TWu8rbdBGCPIJAiAuOc7gSIQQlV1ieiKPrnqUPmF9WDxqMX6efuV6vhIleq31OmCdYzm/phq01rFAeK73K4AV1xxhBRHgHQBIohdClN4XO78gKSOJ1we+Xu5JHuTJ2GLz9/IH4FxKxekRJISonL776zva121Pp+vyHYOszEmiLyaL2YKfhx/xKfFGhyKEcAHe7t5OO5ck+hJwM7lh1TKapRDi2sUmxbI1dist65TdEMVFkUQvhBBO9OamN8m0ZjK923SnnVMSfQmYTWaybFlGhyGEqMSybFloNEkZSU47pyT6EvDz8OOTyE94ZcMrRocihKiknu39LAHeATz5y5NOO6ck+hK4p/U9APz713/LTVkhxDVRKEzKxMW0i047pyT6Eniuz3N8NOQjADKsGQZHI4SojKb8PIXzKed5b/B7TjunJPoSMiszADZtK6KkEELkdT7lPMsOLeOW62+hS3AXp51XEn0JZd+MlUQvRNVj0zZ2ntl5TZ0ybNrGXd/fRVpWGk92d177PEiiL7Glh5YS7BvMddXznTlRCOGCziWf4/WNr9Pk/Sa0/6Q9yw4tK/Ex5u+czy/HfuG9m9+j43UdyyHKgpVmULMqZ+nBpaw8spLnez+P2WQ2OhwhRDk6c/kMKw6vYNmhZSw/vDznvpy/lz/9G/Qv8fHe/fNd3ExuTOowqaxDLZIk+hJ4fdPrNK3dlCe6P2F0KEKIMrT04FLuXXwvHYI6kGnN5GLaRfadsw+0W696PR7s+CAtA1rywLIHeKr7U1T3qF6i42utSUxP5NZmt5bb5CKFkaabEkhITaC5f3OqWaoZHYoQogw9uupREtMTOZd8jtik2JwkP6LZCDZP2My7N7/LpbRLANzf/v4SHTstK41ZG2Zx/OJx+ob1LfPYi0Nq9CVQv0Z9lhxcQv3Z9bFpW56X1prnej/HPzv/0+gwhRAlFOgTyNGEozkJHiDUL5QfD/zIkoNL+Pb2b3Ez2dOlSRWvfmy1Wflgywe8/MfLxCXHMfz64UzuOLlc4i+KJPoSmNplKv5e/piUCRMm+0/H67/7/suGkxsk0QtRCT3a9VEUivAm4XhZvKjhWYOBDQey/sR67v7hblYeWUmbwDYA+fa40VqTlpWW89d+REwEDy1/iB1ndjCw4UBm9JhBn7A+hjTbQAkSvVLKDGwDTmmthyql3gCGARnAUWC81vpiPvtFAUmAFcjSWjtnAOZyMLDRQAY2GpjvtsjTkVxKv+TkiIQQZaFhzYY0qNmA97e8T1xyXJ7u07Wr1eaBjg+wNXYrQJ4RbA/FH2LWhln8sP8HLmdcpmNQRwJ9All5eCVB1YNYdNsi7mx5p2EJPltJavRTgP2Ar+P9GmCGY7rA14AZQEGdQ/tqrc9fe5gVXw3PGiSkJhgdhhCihL7/63tGfTcKL4sXI5qPINQ3FF8PX/w8/fDz8KN3WG/q+tRlx5kdgGNQMq35bMdnPLTiIdxMbtzT+h7q+tRl7fG1nLl8htGtRvP2oLep61PX4E9nV6xEr5QKBoYAs4BpAFrr1bmKRAC3l3l0lYiPuw+nL582OgwhXEZ0dDRRUVGkp6dz/PhxLBYL/v7+tG3blpCQkDI5R3pWOvctuY9O13Xi53t/poZnjQLLZrfRn0o8xX0/3seaY2vo16Af34z8hkCfQABe7PtimcRV1opbo58NPAEU1KfofuC/BWzTwGqllAY+0VrPLVGElYTFbJEhjIUoA5GRkURERBAfH4/WOs+2EydOEBkZibe3N7Vr16ZNmzakpKQQFhZ2Tcl///n9XM64zKNdHy00ycPfib7zvM4AvHXTW0zpMqVSPFNTZKJXSg0F4rTWkUqpPvlsfxrIAr4u4BDdtdaxSqkAYI1S6oDWen0+x5kMTAYIDQ0t/ieoINxMbmRaM40OQ4hKKzo6mo0bN3Lw4ME867WG7Cbu7LyfnJxMcnIyJ0+ezCnn5+dHjRo10FqTlZVFhw4dCAgIYOPGjVy4cIFatWrRvXv3PF8Igd6BKBQbozcyqtWoQuPzsnjlLEdOjqRDUIdSfmLnUVd+Y15VQKlXgDHYk7kn9jb6H7TW9yqlxgEPAv211ilFnkyp54HLWus3CyvXqVMnvW3btuJ9ggrivh/vY13UOqKmRhkdihCVSnR0NJs2beLAgQN51menptz3MXOvy285t4Luf15//fV5En7f+X3Zfno7l54qvDNFSmYKKw6vYPj1w3E3uxf5uZxNKRVZUGeXImv0WusZ2G+04qjRP+5I8oOx33ztXVCSV0p5AyatdZJj+SagYjZilZKbyY0Tl05wLOEYgd6BTp34V4jKKDo6ml27dhEZGZlnfX41+Gw2G5hMf5eJjg4mKiqM+vWjCA2NybN/7n2z3ysFBw8e5NChQwwZMoSAxgFsObWFoU2HFhmvl8WL21tUzluRpelH/wHggb05BiBCa/2gUuo6YJ7WOhwIBBY7trsB32itfy5lzBVSNTd7/9lG7zWimX8z9v9zv8ERCVFxRUdHM3/+fKzWv7sqZifiv5O8YtmycIKCzuDtfZnkZB927WpD584RtGq1n+joYObPH4fVasJstnHzzSs5fdrey6Vt292EhsYQH1+DGjUSMZns3SVPn65DUNA5tNasWLGCP4L/ICUzxanztxqhRIlea70OWOdYblxAmVgg3LF8DGhbqggriad7PY1JmXhvy3scSzhmdDhCVGi7du3Kk+Th6lp8QoIf27d3AJTjZRcTE8qWLScxmWxYrWZAYbUqli0bklNu5872jBjxPXFxdUlO9sbbO5nmzfeRlORHXFwgiYk1CGq4l9U2e+fBEN+y6cVTUclYN2Wkrk9dxrQdA8DXIwu6Ly2EKIzWkJlpZsOGHmze3BVf30RyJ/ls6ekeZGTkbidXeV5Wq5n4eH/69FnHzTev5PjxMA4daobWNn5a3Yu1bstYUOt5TJiZO3RuTvdIVyWJvgxld7/KnoVKCJG/tm3bYjbn///Ezc2Kv38cZ84EkZjoS0DAGZSyYe+pDaC5cKE2HTtGYu+1nbsh3/7eZLLSoEEUJhOYzFY69FzJpfpLWOf3DdbJHaDL+3B0EP90i2RSR+cPG+xsMtZNGbKYLABk2qSbpRCFCQkJYdy4cfz888/ExsbmrM9uvmne/BBNmx5lxYqbAYiLC8ReW9dk19jT0rxo2vQABw82t+9kScGz4Xp8mv1CjSabWe99jiSSSCCBjKb2seQ9rN6oI33RP36OR+wA7prmvM9sJEn0ZcjTzROAUd+N4lD8IZ7p9YzBEQlRcYWEhDB48GA+++yznHW5e9yYTFZatPiLrVuvnI1Jo5QmNPQEoaEnOHzRiq3nTGi+mDRzJmlAhvbFW3njq32pTxh+1uY0dB9BYx8/DldvhmlQIGPHwo03Ou3jGkoSfRnKbroBcsauFkIULCQkhPvvv59ffvmFuLg40tLScm7Gaq1ISKiBj8/Vvbfr1TuJ2WzlTL2lqAcexM3qQZPkXrSrHkCwCsZb2bs3ayAoqDUPPDDSiZ+q4pFEXw7evultpnadanQYQlQKISEhjB8/Hsj78JTJpLnhhu2cOBHMjh3tsdlMKKWpWTOeceO+5oh5H8vUUhqpRtxqupVuHbpRp04dwsLCiIuL46+//qJFixZ07Ojc+VkroiKfjDVCZXwyFiAmMYaQd0KY3GEy/RtePafkueRzrD+5nkGNBpV4lhohqpLIyEhWrFiRM9bNyZP1iIoKIywsyv4+KYPfm0+jtqrJ/dxPh9YdGDmyatfaS/VkrCg+Xw9f3M3uzN0+l7nbCx677XzKeUn0QhSiY8eOBAQEEBUVhZeXFytXriQkJCZn+xo+wwSMVqMZMWSE1NqLIIm+DPl6+HJy6kkupF4AQJP3ryUfdx/G/ThOBj8TohhCQkLyDEC2fPlytNZkkMFJTtKb3twz5B5J8sUgib6MBfoEFvrwhcVkIS0rzYkRCVH5paSk5DTjnOMcAHWpS0pKkWMpCuSBKaezmC1SoxeihMLCwjCZ7OkqO9EHqADCwsIMjKrykETvZBaTRR6oEqKEQkJCCA8Px2Qy4YcfAAFtA8pspilXJ4neyaRGL8S16dixI+Hh4fRt0JdAz0B+SfjlqhmoRP6kjd7JpEYvxLWJjo7m559/xmq1Upva/H7id5IykvD18DU6tApPEr2TSY1eiGsTFRWF1WolSSdxmMN09+8uSb6YpOnGyaRGL8S1CQsLw2w2s4lNZJHFy71fNjqkSqPYiV4pZVZK7VBKLXO8f0MpdUAptVsptVgpVaOA/QYrpQ4qpY4opZ4qo7grLYtJavRCXIuQkBAG3z6YSFMktza6lV6tehkdUqVRkhr9FCD3/HhrgFZa6zbAIRzzyuamlDIDHwI3Ay2Au5RSLa493MrPYraQYc0wOgwhKp1D8Ye44+c7SLel83jvx40Op1IpVqJXSgUDQ4B52eu01qu11lmOtxFAcD67dgaOaK2Paa0zgEXALaULuXKTphshSsZqsxKbFMt9P97H8YvHAftE3aL4inszdjbwBFC9gO33A//NZ309IDrX+xigS3GDc0XZNfrVR1fnrLuyi5i72Z0eoT2wmC3ODk+ICiEmMYaFexZy/OJxfjr4E6eSTgHwTM9neK7Pc3mGBBdFK/JqKaWGAnFa60ilVJ98tj8NZAH5TZR69WSPkG/HV6XUZGAyQGhoaFFhVVq1q9Umy5bFoAWDCi335a1f5sxBK0RVobVm5vqZvLT+JTJtmfh6+NIztCdP9XiKtoFt6Vm/p9EhVkrF+VrsDgxXSoUDnoCvUmqB1vpepdQ4YCjQX+f/5EIMkPvRtWAgNp9yaK3nAnPBPkxxCT5DpTKl6xR61u+JTdvy3R6fEs/wRcNJzkx2cmRCGOPM5TNsi91GsG8wpxJP8ey6ZxnSZAgfhH9AWI0wo8NzCUUmeq31DBw3Wh01+scdSX4w8CTQW2td0MhCW4EmSqkGwClgNHB3GcRdabmb3eka3LXA7aeTTgOg8v1jSAjXcSntEhN+msD3+7+/atvdre+WJF+GStOP/gPsbfZrlFI7lVIfAyilrlNKrQBw3Kz9F7AKe4+db7XW+0oZs0vLHtpYKUn0wrUN/now3+//nundprNh/AbmDZtH7Wq1eaHPC9zV6i6jw3MpJbqjobVeB6xzLDcuoEwsEJ7r/QpgxTVHWMVkt4BJjV64srOXz7Ll1BbubHknrw98HYAeoT2Y0GGCwZG5Jrl1XcFkt91LjV64mjVH1zD7z9nsP7efhLQEzMrMMz2fMTqsKkESfQWT03QjNXrhQj7f8TkTl04kyCeI3mG98TR7Mq7dOFoHtjY6tCpBEn0Fk9N0IzV64QIyrBm8vfltZqydwcCGA/lh1A/4uPsYHVaVI4m+gpEavXAVn27/lIdXPkxqVioA3935nSR5g8jolRVM9jg4Hm4eBkciROk889szNKrViIW3LSRphowbbySp0VcwqZn22o+72b3Ah6quVJLaf/ZfDNlNRAW9L6xMWbiWpimbtpFhzcBismBSJjQarXXOTwAfdx9p9qogEtMT6RvWl9GtRhsdSpUnib6CybLZx4m74393GBxJ5TSh/QTmDZ9XdEFR7iZ3mMy7f77Lw50f5saQG40Op0qTRF/BtAlsw+xBs7mUfqnIsiWpYWt0Ts0/u8Zb1PvCypSGzn+4oyLFJMawYPcCRrUcRcOaDXPiUiiUUkxfMz1ndMOqTGtNpi2TTGsmGdaMnOWi1mXZsgotk/tnhjWjwG3Z2y+mXUSjeWjFQ+x4YIfRl6VKk0RfwVjMFqZ0nWJ0GBXWB+EfFLht8YHFxf4istqsZNmycpJb9nKWLYtM6xXvc22/1m1Xbr9yv5zkWcykXNg6q7aW1eUulMVkwWK2XPXT3eyes9wxqKN0oawAJNELl7L2+Foav9e4yCR8rX9VlJZJmXAzueV5WUyO5FhI0vSyeOWbSC0mS4H7F3XMK9dlx1JQmdw/3Uxuci+kEpFEL1zGP2/4JyG+IVcl0TzvzZZSb7tye0m2mZR0dBPOp8qyJ0VZ6dSpk962bZvRYQghRKWhlIrUWnfKb5tUL4QQwsVJohdCCBcniV4IIVycJHohhHBxkuiFEMLFFTvRK6XMSqkdSqlljvd3KKX2KaVsSql87/Q6ykUppfY4phuUrjRCCOFkJelHPwX7vK/ZQ9DtBUYCnxRj375a6/MljE0IIUQZKFaNXikVDAwBckaL0lrv11ofLK/AhBBClI3i1uhnA08A1a/hHBpYrZTSwCda67n5FVJKTQYmO95eVkq58peIPyB/4RROrlHR5BoVrSpdo/oFbSgy0SulhgJxWutIpVSfazh5d611rFIqAFijlDqgtV5/ZSHHF0C+XwKuRim1raAn2ISdXKOiyTUqmlwju+I03XQHhiulooBFQD+l1ILinkBrHev4GQcsBjpfQ5xCCCGuUZGJXms9Q2sdrLUOA0YDv2qt7y3OwZVS3kqp6tnLwE3Yb+IKIYRwkmvuR6+UGqGUigFuBJYrpVY51l+nlFrhKBYI/KGU2gVsAZZrrX8ubdAuoEo0UZWSXKOiyTUqmlwjKujolUIIIcqOPBkrhBAuThK9EEK4OEn0TqKUaqeUisgeCkIp1TnXtjZKqc2OISX2KKU8jYzVKAVdI6VUmFIq1bF+p1LqY6NjNVJhv0uO7aFKqctKqceNitFohfwudc71e7RLKTXC6FidQmstLye8gNXAzY7lcGCdY9kN2A20dbyvDZiNjreCXaMwYK/R8VWUV0HXKdf274H/AY8bHWtFu0aAF+DmWA4C4rLfu/JL5ox1Hs3f4wT5AbGO5ZuA3VrrXQBa63gDYqsoCrpGIq8Cr5NS6lbgGJDs/LAqlHyvkdY6JVcZT0c5lye9bpxEKdUcWAUo7E1m3bTWJ5RSU4GOQABQB1iktX7dsEANVMg1CgP2AYeAROAZrfUGwwI1WCHXyRv4BRgIPA5c1lq/aVykxinoGjm2dQE+wz5kwBit9WLDAnUSqdGXIaXUL0DdfDY9DfQHHtVaf6+UuhP4FBiA/d+gB3ADkAKsdUzyu9ZJYTvVNV6j00Co1jpeKdUR+FEp1VJrnei0wJ3sGq/TC8A7WuvLSinnBWuQa7xGaK3/BFo6vgzmK6VWaq3TnBW3EaRG7yRKqUtADa21Vvb/hZe01r5KqdHAYK31fY5y/wekaa3fMDBcQxR0jfIptw57+3OVnN+gkN+lDUCIo1gNwAY8q7X+wKBQDVOC36XfgOmu/rskvW6cJxbo7VjuBxx2LK8C2iilvJRSbo4yfxkQX0WQ7zVSStVRSpkdyw2BJtjboauqfK+T1rqn1jpM24crmQ28XBWTvENBv0sNHP/PUErVB64HoowI0Jmk6cZ5JgHvOn7J0nAMyay1TlBKvQ1sxX5jaIXWerlxYRoq32sE9AJeVEplAVbgQa31BYNirAgKuk7ibwVdox7AU0qpTOx/8Tykq8CkSNJ0I4QQLk6aboQQwsVJohdCCBcniV4IIVycJHohhHBxkuiFEMLFSaIXQggXJ4leCCFc3P8DJRRv4W5ryP0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Where in the state do we see Black+Hispanic heavy home districts?\n",
    "minMinor = 0.40 \n",
    "minMinor2 = 0.50\n",
    "print(\"Here's the\",STATE,\"map with Hisp+Black greater than \",minMinor,\"or even\",minMinor2)\n",
    "for t in range(nTracts):\n",
    "    if ((HDvBlack[t] + HDvHisp[t]) > minMinor and tractPop[t] > minTractPop):\n",
    "        if (HDvBlack[t] + HDvHisp[t]) > minMinor2 :\n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='blue' )\n",
    "        else :            \n",
    "            plt.scatter(tractCPx[t],tractCPy[t],marker='.',color='gray' )\n",
    "\n",
    "x,y = tractMAP.exterior.xy\n",
    "plt.plot(x,y,c=\"green\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "58119ef8-13ef-4135-9843-6518025dda07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district population by tract; avg =  775178.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# LET'S VISUALIZE OUR HOME DISTRICT population in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district population by tract; avg = \",np.sum(tractPop)/nDistricts)        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "a = avgDistrictPop\n",
    "ax.hist(HDvPop, bins=[0.9*a,0.92*a,0.95*a,0.99*a,1.0*a,1.01*a,1.05*a,1.1*a])   #n_bins\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "1f9101bd-108a-48cb-bee5-3f4e04c18132",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is a histogram of home-district area by tract\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# more HOME DISTRICT STATS in a histogram\n",
    "n_bins=50\n",
    "print(\"this is a histogram of home-district area by tract\")        \n",
    "fig, ax = plt.subplots(tight_layout=True)\n",
    "# We can set the number of bins with the *bins* keyword argument.\n",
    "ax.hist(HDarea, bins=n_bins)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "d8167a80-2b91-4adc-ab34-9e010838213d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of HD area to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDarea,HDvGOP,marker='.' )\n",
    "ax.set(xlabel=\"tract area\", ylabel=\"home district pct GOP\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "cf95cc7c-e58a-4174-94f6-b6d4c7ccb0ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to red-blue lean?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(HDvGOP,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"home district pct GOP\", ylabel=\"tractUse\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "617384b8-1e0b-45a7-ae09-babe8dd9d429",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# What is the correlation of tract usage to tract population?\n",
    "fig, ax = plt.subplots()\n",
    "plt.scatter(tractPop,tractUse, marker='.' )\n",
    "ax.set(xlabel=\"Census tract population\", ylabel=\"tractUse\")\n",
    "ax.set_xlim(left=0,right=10000)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "91f163ab-6b94-47ee-bd29-be3e751fe2b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# UPDATED VOTES-SEATS CURVE (from votes2seats.ipynb 1/15/22)\n",
    "# LET'S REWORK OUR VOTES - SEATS CALCULATIONS\n",
    "# FIRST, LET'S DO THE BASIC VOTES-TO-SEATS, no fractional seats\n",
    "# INPUTS ARE HDvGOP[nTracts] and HDweight[nTracts]\n",
    "# HDvGOP is the redness lean of each Census tract's Home District\n",
    "# HDweight is the population of this Census tract relative to the state population\n",
    "# stateGOP is the statewide fraction of GOP vote vs. GOP + Dem vote\n",
    "# KEY EQUATION:\n",
    "# expected Seats at a given vote V = sum(HDweight) for tracts with HDvGOP > 0.5 + (V - stateGOP)\n",
    "# for easy calculation, create ~1000 bins, each bin containing HD's in a dV vote window\n",
    "nBins = 1000\n",
    "dV = 1./nBins\n",
    "binWeight = [0.]*nBins\n",
    "\n",
    "for t in range(nTracts):\n",
    "    binNo = int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binWeight[binNo] += HDweight[t]\n",
    "    \n",
    "binVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "for b in range(nBins) :\n",
    "    binVote[b] = b*dV\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "85608d3f-c3bd-4d91-9378-16270ceeb9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BUyxuqoZeAa7HMyeBqq4TkV4u9usCbFXVbQAiMhO4EdiUrYwCoeJ0n7wAOAp4HwzFmNKgcWOfHer0mjWc3forDV58odCyQ1/7jqRc8wZUCQlixu+72WOhptjcJAJUdVeuru7pLnZrBOzK9n43kLvl6jWcBLMXCAVGqKqbCZKMCaz33vPZoRJjYwm64AJqDhjgtdxv31yZ75hB0YPbWhIw58XN46O7RKQ7oCJSWUSewFNNVIj8+hrkHv38emAt0BDoCLwmInnG3BWR+0RktYisPnTokItTG1M2pCUmcnzh59QaMoSg6tULLFdQu0Cv1nWtUdicNzeJYDROXX4jnG/1HT3vC7MbaJLtfWOcb/7Z3Q3MUcdWYDtwWe4Dqeo0VQ1X1fB69eq5OLUxfjZmjLOcp+S5H6NnzxbadyC/doGOjWvZ46HGJ9w8NXQYuKMYx14FtBaRFjh9D0YCt+cqsxO4FvhWRC4C2gDeZ80wpjRYu/a8D6GqJM2aRbWOHana5tICy+XXLlCrWghzH+xx3jEYA+7GGmoBPAQ0z15eVb3On6eqaZ4OaJ/jPD76lqpuFJHRnu1v4PRLmO6ZDlOAsZ7EY0y5d+r7VZzdvp0GEyYUWKagdoGx/W3eAOM7bhqL5wJvAp8ARWrIVdX5wPxc697I9novcF1RjmlMeZEUG0tQzZrUHNA/3+1jZv5g7QKmRLgaYkJVp/g9EmMqkLSjRzn25ZfUvm0kQVWr5tk+Y+XOfEcStXYB4w9uEsGrIhINfAGkZK5U1TV+i8qY0u7Sguv03Uj+6CNITaV2AY3Er3z1c551zepUt3YB4xduEkE74Dc4I45mVg0pNgKpqcimTSv2rpqRQWLsLKqFd6LKJZfk2T5x/mYOHj+bZ/3LIzoW+5zGeOMmEQwDWqpq3n+ZxpgiO7ViBak7d1LvoQfzbJuxcidvLMn74NzoXi2t05jxGzf9CNYBYX6Ow5iy5b77nKUYEmNnERwWRuh1OZ+TiE9I5E/5TDN5+cWhPD3QnhIy/uPmjuAi4CcRWUXONgKvj48aU6798kuxdks7dIjjixZR5ze/IahKlRzbHp+1Nk/Xe4AXhrUr1rmMcctNIoj2exTGVBBJcz6CtDTCbr0la118QiIPvBfP/uMpecpblZApCW4SwWrgtKpmiMilOENALPBvWMaUP5qRQdLs2VTv2pUqLVoATpvAs/lUB4FVCZmS46aNYAlQVUQaAYtwxgea7s+gjCmPTi5dRuru3VmTz3hLAoJVCZmS4+aOQFT1lIiMAv6hqn8TkbV+jsuY0q1jxyLvkhg7k+A6dQjt25cxM3/It8MYOBPOv3ZHJ6sSMiXGVSIQkQicgedGedYF+y8kY8qAV14pUvHUAwc5sTiOC+++i9/9N/+hI8AmnDeB4aZq6BHgGeAjz6BxLYHF/g3LmPIl6cMPID2d2HpXWxIwpY6bYaiX4LQTZL7fBjzsz6CMKfXuvNP56WKmMk1PJ2n2Bxy8tAOTN5/Ot4wlARNIrqaqNMbksnu366Invv2WtH37mNa4X55twUHC8zdeaaOJmoCyRGCMn+2Y/j+OVQllRYO2ebZZEjClgZs2AmNMMaXu20fQyqV80awL6UE5n7EY2rGhJQFTKhSYCETkHyIypaClJIMsraZMKdqv4YUXXmD69Ok+jWHHjh3MmzfPp8fMPG6fPn245pprGD9+fL5lqlWrRmRkJJGRkbz55psAnDp1iuHDhxMZGcmwYcNISkryeWxlyfRnXwWFBc1y1v+P7tWSV0ZeFaCojMnJ2x3BaiDey1LhFTUR+IO/EsHTTz9NTEwMS5cu5euvv+ann/JOnt6oUSPi4uKIi4tj1CjnyeJp06YRHh5OXFwcI0eOZNKkST6PrVSIiHAWLx7932ou+2Ex8fUv5WCNOlnrR/dqaT2GTalSYCJQ1XdU9R3gVObr7OtKLsSieeaZZ+jduzcRERF8+umnqCpDhgwhLi6OU6dOERERwfbt24mLi+P666/n5ptvpmPHjsyePRuAXbt2MWjQIPr06cOgQYM4dOgQALGxsXTr1o2oqCheeuklZsyYwZ49e4iMjOTFF18kNTWVe++9l6ioKHr06MH3338PwJIlS+jYsSNDhgxh3bp1eeLdv38/vXr1IioqisjISI4dO0ZycjK33nor1157LX369GHr1q0AjB07lqioKK6++mqmecbDf/nll/nss8+IjIwkPj6eJ554goiICKKiooiNjS3273Ht2rX07NkTgEGDBrFkyZI8Zfbv30/v3r256aab2LFjBwC//PIL4eHhAHTp0oXFi8vpk8YTJjhLAWas3Mm+LxZR98wxFjQ/lzCa1aluScCUPqrqdQHWuFlXUkunTp20IAsWLND7779fVVVPnjyp7du314yMDD148KCGh4fryJEjdebMmaqqunjxYm3btq2ePXtWk5OTtXXr1pqenq4jRozQ5cuXq6rq3Llz9fHHH9fDhw/rlVdeqSdOnFBV1bS0NFVVveSSS7LOPXXqVJ0wYYKqqu7fv1+7d++uqqqdOnXShIQEzcjI0H79+unbb7+dI+YPP/xQn3nmGVVVzcjI0IyMDB07dqy+//77qqq6du1avfnmm1VVs85/5swZbd26tZ49e1YXL16so0aNyjreFVdcoampqaqqmp6enuNcp06d0t69e+dZJk+enOd32bp166zXb731lo4fPz5PmUOHDqmq6sKFC7VPnz6qqvr666/r448/rqqqr732mrZp0ybPfuXVXQvu0rsW3KWrdxzVK/68QN/pNVSXdOiiLZ78WJuN/VSbjf1UV+84GugwTQUFrNYCPlcLfGpIRAYAA4FGudoEagJpfsxNxbZ+/Xq++eYbIiMjAUhJSeHIkSPUq1eP6667jo8++oj3338/q/xVV11FpUqVqFSpEvXr1+fQoUOsX7+ep59+GoC0tDRatWrFr7/+Svv27alRowYAwcF5O1avX7+eZcuWsXDhQgCSk5MBOHbsGE2bOg2CXbp0ybPfoEGDWLduHXfeeSdNmjQhJiYm6zreeOMNAEJCnD/T1KlTmTt3LsHBwRw8eJCDBw/mOd7EiRO55557CAoK4sknn6Rt23NPqlSrVo24uDhXv8ugoHM3i8nJydSpUydPmbp16wJw/fXX88ADDwAwatQoHnvsMaKiooiIiKBhw4auzlfm3Hyz8/PDD3OsPngshZunLqP+yaN0OvAz77fpS4ankXj8sHY2bIQplbw9ProXp51gCDnbBI4Dj/ozqOJq27Yt1113Ha+++ioAZ8+epXLlymzYsIFly5YxZMgQpkyZwsMPO/3h1q5dS1paGqdPn+bAgQPUrVuXtm3b8swzz3DVVVdlHePEiROsX7+e06dPU61aNTIyMggKCiIkJCTrddu2bWnVqhWPPvpo1n4AoaGh7N69m8aNG7Nq1SpatWqVI+b09HRiYmIAuPfee/n8889p27YtERERDBs2LOtYiYmJvPXWW6xfv57U1FTatGmDqlK5cmXS0py8rKr07duXwYMH89133zFu3Dg+zPZBdfr0aQYMGJDn9zZkyBAee+yxHOs6dOjAsmXL6N69OwsWLOCVXEMqnDhxgmrVqhEcHMyPP/6YlRQqV67Ma6+9BjjtBY0bNy7Kn7DsOHIkz6rjZ9LYdvgEAP0TVqLAQk8j8fhh7ewJIVNqFZgIVHUdsE5EPgJOqmo6gIgEA1UK2i+QBg4cyPLly4mMjEREaNy4MdOmTeO+++7jvffeo2nTplx33XVZdd8NGzbklltuYfv27bzwwgsEBwczefJkHnjgAU6ccP5D33PPPdx55508++yzREZGUr16dfr378/YsWMZPnw4gwYNYsCAAfzhD3/goYceIioqCoDw8HAmTZrE5MmTGTx4MA0bNiQ0NDRPzHFxcYwfP56QkBCqVKlCjx496NWrF6NHj+Yf//gHqsoNN9zAY489Rtu2benRoweXX345F154IQDt2rXj119/Zfjw4URHR/PQQw8BcObMGcaNG5fjXEW5I5gwYQKjRo3i7NmzDBgwgMsvd+q177jjDv73v/+xadMm7r//fkJDQxER/vWvfwGwadMm/vjHPxIcHEz79u3Lb2NxPnYddZrOgjPSuT7he1ZfdBmHq4fZY6Km1BOn6shLAZEVQF9VPeF5fwHwhap2L4H48ggPD9fVq1ef93Hi4uJ47733+M9//uODqEyF46l+xJNY4xMSufOz3wFw9fIInlv1LtHd7uFseARzH+wRmBiNyUZE4lU1PL9tbnoWV81MAgCqekJEqvssOmPKgX9982vW64E7VnCwWhjVrunBrN97f8TUmNLATSI4KSJXq+oaABHpBOQ/clYZktkRyphiufbarJfxCYl8uekAVZvCRUlnufpQAgvDB/OOJQFTRrhJBGOA2SKSOYtGA2CE3yIypiz485+zXs5Zsztr0vl+G4+RLkGc6DsoMHEZUwxuhqFeJSKXAW1wZtD7SVVT/R6ZMWXE0q3O/AIh6UqfTcdYefEVDIxqH+CojHHP7eijbYArgKrAVSKCqr7rv7CMKeU8j+HO+Mu/2HHEeVqoy68nCDudztrekYyy/gKmDCk0EYhINBCJkwjmAwOA7wBLBKbiOu00k721dHvWqus3JHOgZggRI6xayJQtboahHg5cC+xX1buBDrjsRyAi/UXkZxHZKiJPF1AmUkTWishGEfnGdeTGBNjxM2lsPeg8UNfoxCHa7T7NN+1rc3tE88AGZkwRuakaOq2qGSKSJiI1gYNAy8J28nQ8+yfQD9gNrBKReaq6KVuZMOB1oL+q7hSR+sW5CGMCYefRc2MvDtixgrQg2N67nA6pYco1N4lgtecD+984Q02cAL53sV8XYKs6cxwjIjOBG4FN2crcDsxR1Z0Aqpp38BxjSqHjZ9I4fsZ5ZqJSeip9d64ivlUoUq9agCMzpui8JgIREWCCqiYBb4jIQqCmqv7o4tiNgF3Z3u8Gcs/OfSlQSUTigFDg1fwaoUXkPuA+IGsAN2MCKfai9hyolgLANXvXU+vsKdZGXBLgqIwpHq+JQFVVROYCnTzvdxTh2JLfIfM5fyecNohqwHIRWaGqv+SKYxowDZwhJooQQ05uOpDdcAM88cS58nfd5SyHD8Pw4YXvn7v844/D4MHw889w//2F75+7/Pjx0L07LFsGzz5b+P65y//rX9CmDXzyCUyeXPj+uct/8AHUrQvTpztLYXKXzxzb6P/+Dz79tPD9s5dfvvzc6J7PPOO89+bCC3OWP3IEPPM2cN998MsvBe8LcOmlOctfeOG5OQduvjlroLkDx1K48vAJXrh9IuDpSRxal+QOdQu/PmNKITeNxStEpHMxjr0baJLtfWOcEU1zl1moqidV9TCwBKcx2phS6+DxM1mvmxw/QLsj20gdcCME5ffdx5jSz82gc5twqnASgJM43/RVVb32mBGREOAXnG/7e4BVwO2qujFbmcuB14Drgco4bQ8jVXVDQcf11aBzxhTXrW8s47Hxzt3d4isjuGHHMi7/9ht+v8q5k3y7/9uBDM+YfBVr0DkRaaGq23H6DRSZqqaJyIPA50Aw8JaqbhSR0Z7tb6jqZk+7w49ABvAfb0nAmECLT0gkfmei551y7a7VHO/cgxDPsODGlEXe2gg+wKm/f0tVr/VSrkCqOh+nE1r2dW/kej8JqDiD1psybc6a3aRnOK8rBSvVU0+zJuI6AjImuzE+4i0RBHl6FV8qIo/l3qiqL/svLGNKpy0Hjme9rhySwe4L6nFZ/8jABWSMD3hrLB4JnMFJFqH5LMZUKPEJicQnONVCQaKEBMPWrv3o1DzvfM7GlCXepqr8GXhJRH5U1QUlGJMxpdKKbUfI8DxbsbfexbQ6sY8uo38T2KCM8YFCHx+1JGCM4/jpVBSokpZCozNH2Ht1Dzq1ax7osIw5b276ERhT4cUnJDLt220A9N6zjgvSzvBLB5uL2JQPlgiMcWHOmt1Z1UIDdiwnPQMGvPtqYIMyxkfczEdQCfgD0Muz6hvgDZulzFQkh4474wq1TNrDZYm7OJ0WRLM6VQMclTG+4Wb00alAJZzhogF+41l3r7+CMqa0STp1FnDGFUoJCqFKSAihVd1O8GdM6ebmX3JnVc0+/s/XIrLOXwEZU9rEJySyOiGRqmkpRO1ew5JGHblh30+BDssYn3HTRpAuIlnj64pISyDdfyEZU7pktg9E7v6B6mkpLGzRjXoXuJqkz5gywc0dwZPAYhHZhjPgXDPgbr9GZUwpktmbeOD25Wyv2YALrupIaPcaAY7KGN8pNBGo6iIRaQ20wUkEP6lqit8jM6YUyOxN3DpxF62T9/DP9sNodXFNGHZXoEMzxmfctnZ1App7yncQEfKbScwUYMHTsH99oKMwxXBx0mn+V+kU1XaeQoPhzpaLaHtgNUzxPEsaWvlcYTng/Hx7UMkHakrWxe1gwMRAR+Ezbh4f/S9wCbCWc20DClgiMOVeekYGpCqVdqWS2rQSdWtXIbRKJXhlhVNgbLfABmiMD7i5IwgHrtDCZrAxBStH3xwqmode+47G2xbwYNocnmz6By6qezX/vbsrvBPpFLj7s3OFF3qazmxiGlPGuHlqaANwsb8DMaa0iU9IZMOeJAZsX8HWWo34JawJA65sEOiwjPE5N3cEdYFNIvI9kNVIrKpD/BaVMaXAnDW7aXVkJ5cc28uUDjfTpUUdbu/aNNBhGeNzbhLBX/wdhDGl0ZYDxxm4YzmnQqoQ1/gqelavXPhOxpRB3uYsFnV8U1gZ/4RmTODEJySyectentmzjq+bdOJ0parUDc3WiewPfwhccMb4mLc7gsUi8iHwsaruzFwpIpWBHsDvgMXAdL9GaEwAzFmzm6id8VRNT2V+824ECdx8deNzBUaMCFxwxviYt0TQH7gHeF9EWgBJQFUgGPgC+LuqrvV3gMYEgqoyYMdyfg5rwq9hjel3+UV0alb7XIFdu5yfTZoEJkBjfMjbVJVncEYcfd0zFHVd4LSqJpVQbMYETOjWTTQ/foC/d7wFgEvq5hpS4jeeKSrj4ko2MGP8wFXPYs/cA/v8HIsxpUJ8QiKhX37KyZCqfNO4IwAb9x0LbFDG+JHNUGZMLqvX76DnnnV83eRqUkKcBmLrP2DKM0sExuTSMv4bKmekMb+5M3zE0I4Nrf+AKdcsERiTjapS/fN5bK7djB21GgJQo4rNRGbKNzeDzt0EvATUxxmGWgBV1Zp+js2YErf208XUObqPt68693hovh1lHn+8xGIyxt/cfNX5GzBYVTf7OxhjAm3fezOoW6ka3zZyZmfN038g0+DBJRyZMf7jpmrogCUBUxGkJSbSZMNKFjXpREqIM5xEeLPaOfsPZPr5Z2cxphxwkwhWi0isiNwmIjdlLm4OLiL9ReRnEdkqIk97KddZRNJFZLjryI3xseQ5HxGSnsaC5ufmGGh1UWj+he+/31mMKQfcVA3VBE4B12Vbp8AcbzuJSDDwT6AfsBtYJSLzVHVTPuVeAj4vQtzG+JRmZLB/xkw2X9iCnTWdUddDgiX/aiFjyhk3cxYXd6L6LsBWVd0GICIzgRuBTbnKPQR8CHQu5nmMOW+nVq4kaM8uPut0W9a6q5uE5V8tZEw5U2jVkIg0FpGPROSgiBwQkQ9FxM3XpEbArmzvd3vWZT92I2AY8EYhMdwnIqtFZPWhQ4dcnNqYokmMncWJKjX4rmH7rHVHT54NYETGlBw3bQRvA/OAhjgf5J941hVG8lmX+0m8V4CxqpqeT9lzO6lOU9VwVQ2vV6+ei1Mb417a4cMc+/JLvmzcidTgSlnrW9S7IIBRGVNy3LQR1FPV7B/800VkjIv9dgPZh2ZsDOzNVSYcmCki4AxqN1BE0lR1rovjG+MTSXM+QtLT+SxbI7EAo3tfUvBOzz3n/8CMKSFuEsFhEbkTeN/z/jbgiIv9VgGtPUNY7wFGArdnL6CqLTJfi8h04FNLAqYkaUYGSbNns71xG/aE1s9a37l5AY+NZurbtwSiM6ZkuKkauge4FdiPMwLpcM86r1Q1DXgQ52mgzcAsVd0oIqNFZHTxQzbGd04uW07qrl3MahCeY32Bj41mWrvWWYwpB9zcERws7kT1qjofmJ9rXb4Nw6p6V3HOYcz5SIqdyZkaoSy9uF3WugJ7E2c3Zozz0+YjMOWAm0SwQUQOAN8CS4Clqprs37CM8b/UAwc5/vViVnfsR2rwuf8KBfYmNqacKrRqSFVb4bQLrAduANaJyFo/x2WM3yXP+RDS05lep2OO9YVWCxlTzrgZfbQxcA3QE+gAbAS+83NcxviVpqeTOHs2+y5px54adbPWu6oWMqaccVM1tBPnCaDxqmqNvKZcOPndd6Tt3ceSQUNzrLdqIVMRuUkEVwE9gNs9A8dtAb5R1Tf9GpkxfpQYOwutXYf3gpvnWO+6Wmj8eN8HZUyAuBlraJ2I/Ar8ilM9dCfQC7BEYMqk1P37OREXx6pug0gLCs5aLxShWqh7d/8EZ0wAuGkjWA1UAZbhtA30UtUEfwdmjL8kzf4AVJnfpAuknFvfMKyq+2qhZcucn5YQTDngpmpogKraSG+mXNC0NJI++IAa11yDNmgIOxKztl3RsJb7Az37rPPT+hGYcsDN46OWBEy5cWLJEtIOHOBon0Gs2ZWUtT44qJCxhYwpx9wMMWFMuZEYG0tI/fp8ULkZaennBsO99rKL7GkhU2FZIjAVRuqePZxc8i1hw2/ml8OnAx2OMaWGm4lpbhGRUM/r50Rkjohc7f/QjPGtxA8+ABF2RfRjdUJijm11Q6sEKCpjAs9NY/GfVXW2iPQArgf+D5gKdPVrZMb4kKamkvzBh1zQsyd/33ySjGxTJBWrN/Err/g0PmMCyU3VUObsYYOAqar6MVDZfyEZ43vHFy8m7dAhjvQZxJebDuTYVqzexB07Oosx5YCbRLBHRP6FMyfBfBGp4nI/Y0qNpNhZhFx8MR8EN8kzX2qxBpn76itnMaYccFM1dCvQH/g/VU0SkQbAk/4NyxjfObtrFyeXLqXugw+SERycY1uxB5l74QXnp81UZsoBN9/s/6Wqc1R1C4Cq7gN+49+wjPGdpFmzITiYsFuGs/voqRzbhnRoaI+NmgrPTSJom/2NiAQDnfwTjjG+pWfPkjRnDhdERjJ7RwpLthzOsf3IybMBisyY0qPARCAiz4jIcaC9iBzzLMeBg8DHJRahMefh+Ndfk37kCLVH3Ersqp15tg+4skEAojKmdCkwEajqBFUNBSapak3PEqqqF6rqMyUYozHFlhgbS6WGDalxzTVUCcn5z/3yi0O5vWvTAEVmTOnhZhjqZ0SkNtAaqJpt/RJ/BmbM+Tq7Ywenlq+g3phHeH/1HlZlG2AuOAheGNbOy96F+Ne/fBChMaWDm2Go7wUeARoDa4FuwHKgj18jM+Y8Jc6aDSEhJHS5lj/NXp/jsdFOTc9zJrI2bc47PmNKCzeNxY8AnYEEVY3CmbHMRiQ1pVrG2bMkf/QRoVFRzEk4g+bqPJCSlnF+J/jkE2cxphxw04/gjKqeERFEpIqq/iQi9nXIlGrHv/iS9MREwkaOYM2qxDzbR3Q+z7aByZOdn4MHn99xjCkF3CSC3SISBswFvhSRRGCvP4My5nwlxcZSqUkT5gU1YvP+jTm2dW5e2xqJjcnGTWPxMM/Lv4jIYqAWsNCvURlzHlK2bePUqlXUe/wxXv/m1zzbnx5weQCiMqb0cjVmkIj0EJG7VfUbnIbiRv4Ny5jiS4qNhUqVSOjSh91JZ3Jsa1SUeYmNqSDczEcQDYwFMvsOVALe82dQxhRXxpkzJM39mNC+1zJn26k82x+Iah2AqIwp3dy0EQzDeVJoDYCq7s2cqMaY0ub455+TkZxM7REj2LL2eI5tPu1A9t//+uY4xpQCbqqGzqqqgvMYtojUcHtwEekvIj+LyFYReTqf7XeIyI+eZZmIdHAfujF5JcbOonKzZvx08aV5ZiG7ypdVQk2aOIsx5YCbRDDLMx9BmIj8HvgK+HdhO3kGp/snMAC4ArhNRK7IVWw70FtV2wPPA9OKErwx2aVs2cLpNWsIGzGCOT/sOf9ZyLyJjXUWY8oBN08N/Z+I9AOOAW2Acar6pYtjdwG2quo2ABGZCdwIbMp27GXZyq/A6b1sTLEkxs5CKlWi1rChbIndnGNbsWYh82bqVOfniBG+O6YxAeJmiIkawNeq+qWnI1kbEamkqqmF7NoI2JXt/W68z3M8ClhQWDzG5Cfj9GmSP/6Y0OuvZ90x8lQLFWsWMmMqCDdVQ0uAKiLSCKda6G5guov9JJ91uWcJdAqKROEkgrEFbL9PRFaLyOpDh2x0C5PXsfkLyDh+nNojbmXOmt3+rRYyppxxkwhEVU8BNwH/8HQwy13Xn5/dQPbWtMbk0yNZRNoD/wFuVNUj+R1IVaepariqhterV8/FqU1FkzgrlsqXXEK18PA83zauvfwi6ztgjBeuEoGIRAB3AJ951rl57HQV0FpEWohIZWAkMC/XgZsCc4DfqOov7sM25pwzP/3EmXU/UvvWWxARrmxYK8f2qDb1AxSZMWWDmw/0R3A6k32kqhtFpCWwuLCdVDVNRB4EPgeCgbc8+4/2bH8DGAdcCLwuIgBpqhpevEsxFVVibCxSpQq1brwRgLk/7M6xfePeZN+f9IMPfH9MYwLEzVNDS3DaCTLfbwMednNwVZ0PzM+17o1sr+8F7nUbrDG5ZZw8ybF5n1Czf3+Cw8KYsXIn3+/I2VCcb8PU+apb1x9HNSYgXI01ZExplTx/PhknTxLmeYzz9cVb8pTxS0Px9OnOYkw5YInAlGlJsbOo0ro11a7qyIyVO/MMMte5uY/7D2SyRGDKETeDztk9sCmVTm/YyJkNGwgbMQIRIXbVzjxlbMhpYwpXYCIQkcEicghYLyK7RaR7CcZlTKGSYmORqlWpNcSZJaxKSM5/zn67GzCmnPF2R/Ai0FNVGwA3AxNKJiRjCpd+4gTJn31GzYEDCa5Zkxkrd7IqWyNxcJDdDRjjlrdEkKaqPwGo6krA+uibUuPYp5+ip05Re+QI4hMSeW7u+hxPB3VqancDxrjl7fHR+iLyWEHvVfVl/4VlTMFUlcTYWVS5/HKqtmvHnLkbcgwpAZCSluHfIObPL7yMMWWEtzuCf+PcBWQuud8bExBn1q8nZfNmao+4lTU7k5ixMm8j8YjOfp6cvnp1ZzGmHCjwjkBVY0oyEGPcSoyNRapXp+YNNzDny+15OozVqV7JdzORFeT1152ff/yjf89jTAnw+vioiAwQkSUiclhEDonINyIysKSCMya39GPHOPbZfGoNGkTwBRew5cDxPGVuDS+BmcNmzXIWY8qBAu8IPLOR3Q88Baz2rA4HJopIY1W12cRMiUue9wl65gxhI5xG4tzzDlx+cShPD7SnhYwpCm93BI8C16nq16p6zLN8jTP15KMlE54x56gqSbGxVG3blmpXts133oEXhrULXIDGlFHeEoGo6tHcKwuaM8AYfzv9w1pStmwhbKQzrlDuaiGbd8CY4vGWCI6JSIfcKz3r8lbMGuNnSbGxBNWoQa2BA/MdZdTmHTCmeLwlgseBeSLyF89wEzeISAzwMfCYl/2M8bn0pCSOLVxIzSGDCapRg7eWbs9TJvHU2ZILKC7OWTx27NjBd+O+47I6l2Wta9WqFQDTp0+nRYsWREZG0rVrV0aPHk1ysrs5EqZMmeKTMt6sXbuWSZMm5Vn/wgsvMN0PA+vFxcXx448/+vy4AAsXLiQiIoKIiAg+//zzAsu99dZbVKpUKev96tWr6datG71792bgwIEcP+581z18+DAjRoygT58+XHfddX6JuTQoMBGo6nc4k80HAXcB93hed/NsM6bEJH/8MZqSQm1PI/GvB0/k2B4k0K3lhQGKztGsZjPGdsl32m1GjRpFXFwcK1eupE2bNjzyyCOujlkSiaBjx448+eST53WMovBXIkhPT+epp55iwYIFLFiwgCeffJL09PQ85c6cOcOcOXNo0uTc02UTJ07kpZde4ptvvqFLly689957AIwZM4Zx48bx9ddf88UXX/g85tLC6+OjqrpfVcep6s2qepOq/llV95dUcMbAuZ7EVTu0p+pllzFnze48fQdeGNquzLQPPProo3z77bdkZOTs/fzEE08QERFBVFQUsbGxvPzyy+zZs4fIyEjefPNNFi9eTFRUFD179uTGG2/kzJkzzJgxI6vMiy++SGpqKvfeey9RUVH06NGD77//npSUFCIiIgD4xz/+kfV60qRJzJgxg7i4OO6915kfasmSJXTs2JEhQ4awbt26rNhmz55Nz5496dGjB3/961/zXFNERASHDx8GYOnSpdx1110AxMTEEBERQdeuXfnss884evQo06dP58UXXyQyMpL09PRCj+3Wli1baNGiBWFhYYSFhdGiRQt+/fXXPOWmTJnC6NGjCQo69/HXtm1bkpKSAEhMTKR+/fqkp6ezYcMGJk+eTO/evXk9s+9IeaSq+S7AjcAD2d6vBLZ5llsK2s/fS6dOndRULBlpaZo45yM9Hhenqqq3TF2qzcZ+mrXcMnVpgCNU3b59u4aFhWnv3r2zlksuuURVVd9++219/vnnc5Tv2rWrHjhwIMe6K664QlNTU1VVNT09XVU16xiqqidOnMh6/dRTT+k777yTp8zUqVN1woQJqqq6f/9+7d69u6qq9u3bVw8ePKi33HKLDh48WJOSknTAgAG6d+9eXbx4sY4aNUpVVTt16qQJCQmakZGh/fr107fffluPHj2qXbt21bNnz6qq6tChQ/XHH3/MEfsrr7yiU6ZMUVXV+++/XxctWqQ//PCDXnvttZqRkaGJiYnaunVrTU9P1+joaP3vf/+rqurq2MuWLcvxe81cFi1alKPc0qVL9Xe/+13W+9/+9re6bNmyHGWOHj2qgwYNyvN7W7dunTZt2lTbtm2rERERmpqaqnv37tWgoCBdu3atpqSk6DXXXKObNm3SsgpYrQV8rnoba+gpnAnnM1UBOgM1gLeB2b5PS8bkJcHBhA0bCpBvI3FY9coBiCqvTp068dVXX2W9z2wjyM+hQ4eom2u6y4kTJ3LPPfcQFBTEk08+Sdu2bXNs37hxI8899xwpKSkcOHCAmjVr5jnu+vXrWbZsGQsXLgTIaouIiopi0aJFnD59msGDB7No0SIOHTpEgwYN+Pnnn7P2P3bsGE2bOr2yu3TpAsDWrVtJSEigX79+ACQlJZGQkEC7duce1b399tsZOnQo999/PytWrGDq1KnMmjWLbt26ISKEhYVRv379rLuGTG6OHRERQVy29piC1KlTJ+tbfea116lTJ0eZCRMm8NRTT+XZd/To0cyZM4dOnToxYcIE/v73v/Pwww/TsGFDOnRwnpmJjIxk/fr1XH55+eun4i0RVFbVXdnef6fOo6NHRKSGn+MyJo/4hET+NHd9nvV1Q6sEIJrimzJlCtdcc02OqglVpW/fvgwePJjvvvuOcePG8eGHH+Yo8+KLL2ZVtTz11FOZd+qEhISQkZFBUFAQbdu2pVWrVjz6qNPV5+xZpwG9T58+PPLII1x33XX06dOHO+64g86dO+eJLTQ0lN27d9O4cWNWrVpFq1ataNmyJa1ateKrr77KOlfmuTPVq1ePunXr8re//Y1BgwYhIrRp04Z///vfqCrJyckcPHiQunXrUrlyZdLS0gBcHXv58uU888wzeWIdN24cffr0yXrfunVrtm/fzrFjxwDYvn17nmT8yy+/MH78eMaPH8++ffsYMWIEsbGxqCr16tUDoH79+mzdupUqVarQsmVLdu3aRZMmTYiPj+emm27y9qcts7wlghwVrqr6YLa39fwTjjEFm7NmN5rPTPR+mZPYx958802++uorTp8+Tfv27fM08KalpTFgwADAacwcN24c4HwbHjZsGCNGjGDkyJGMGjWKNm3aUKtWraw7guHDhzNo0CAGDBjAH/7wBx566CGioqIACA8PZ9KkSXTu3JmffvqJiRMn0qpVK/bv35/jQzTT5MmTGTx4MA0bNiQ01Blb8sILL2TMmDH06dOH4OBgKlWqxLvvvsvFF1+cY9/f/va3jBw5kg0bNgBOI3T37t2JiIggIyODyZMnExQURL9+/RgzZgyffvops2bNKvTYbu8IgoODmTBhAtdffz3gfPsPDg5m//79TJo0icmTJzN37tys8q1atSI2NhZw7sZuvfVWqlatSlBQUFZj8auvvsqdd95Jamoqffr04eqrry40jrJIcmffrA0i/wPiVPXfudbfD0Sq6m0lEF8e4eHhunr16sILmnInctJidhw5lWPd6F4tbUgJY1wQkXhVDc9vm7c7gkeBuSJyO7DGs64TTlvBUJ9GaEwhJs7fnCcJdG5e25KAMT7gbRjqg0B3EekDZLZafabOeEPGlKgZ39vE9Mb4i7c7AgA8H/z24W8CZuL8zRw7k5Zj3eUXh5aZfgPGlHZeO5QZE2jxCYm8sWRbnvU2yqgxvmOJwJRqf87ncdHOzW1iemN8yRKBKbXGzPyBTfvyDnRrbQPG+JYlAlMqzVi5k7lr9+ZZP7pXS7sbMMbH/JoIRKS/iPwsIltF5Ol8touITPFs/1FEymdvDVMk8QmJ/PWTjXnW2zSUxvhHoU8NFZeIBAP/BPoBu4FVIjJPVTdlKzYAaO1ZugJTPT9NBRSfkMhjsWtJOHoq3+3WQGyMf/gtEQBdgK2qug1ARGbijGiaPRHcCLzrGRlvhYiEiUgDVd3n62DiExKZuGAzG/ckk5Kegaozhr2IkKGa9V6hRLcF+vylJW4B0vPv5A5YlZAx/uTPRNAIyD5o3W7yftvPr0wjwKeJID4hkVvfWJbng8aZ+FxzvS/pbYE+f+mI25teretalZAxfuTPNgLJZ13u//puyiAi94nIahFZfejQoSIHsmLbEa/fNk3p1at1Xd4dZbWFxviTPxPBbqBJtveNgdyPgbgpg6pOU9VwVQ3PHCq2KLq1vJDg/FKOKbVCqwQzflg7SwLGlAB/Vg2tAlqLSAtgD84kN7fnKjMPeNDTftAVSPZH+0CnZrWZNbq7tRGU8riDg4Kod0Fl/hjVmtu7NvX1PwNjTAH8lghUNU1EHgQ+B4KBt1R1o4iM9mx/A5gPDAS2AqeAu/0VT6dmtZk9uru/Dm+MMWWWP+8IUNX5OB/22de9ke21Ag/4MwZjjDHeWc9iY4yp4CwRGGNMBWeJwBhjKjhLBMYYU8FZIjDGmApOnAd3yg4ROQQkFHP3usBhH4ZTFtg1Vwx2zRXD+VxzM1XNt0dumUsE50NEVqtqeKDjKEl2zRWDXXPF4K9rtqohY4yp4CwRGGNMBVfREsG0QAcQAHbNFYNdc8Xgl2uuUG0Exhhj8qpodwTGGGNyKZeJQET6i8jPIrJVRJ7OZ7uIyBTP9h9F5OpAxOlLLq75Ds+1/igiy0SkQyDi9KXCrjlbuc4iki4iw0syPn9wc80iEikia0Vko4h8U9Ix+pqLf9u1ROQTEVnnuWa/jWJcEkTkLRE5KCIbCtju+88vVS1XC86Q178CLYHKwDrgilxlBgILcGZI6wasDHTcJXDN3YHantcDKsI1Zyv3Nc4ouMMDHXcJ/J3DcOYFb+p5Xz/QcZfANT8LvOR5XQ84ClQOdOzncc29gKuBDQVs9/nnV3m8I+gCbFXVbap6FpgJ3JirzI3Au+pYAYSJSIOSDtSHCr1mVV2mqometytwZoMry9z8nQEeAj4EDpZkcH7i5ppvB+ao6k4AVS3r1+3mmhUIFREBLsBJBGklG6bvqOoSnGsoiM8/v8pjImgE7Mr2frdnXVHLlCVFvZ5RON8oyrJCr1lEGgHDgDcoH9z8nS8FaotInIjEi8hvSyw6/3Bzza8Bl+NMc7seeERVM0omvIDw+eeXXyemCZD8ZifO/WiUmzJlievrEZEonETQw68R+Z+ba34FGKuq6c6XxTLPzTWHAJ2Aa4FqwHIRWaGqv/g7OD9xc83XA2uBPsAlwJci8q2qHvNzbIHi88+v8pgIdgNNsr1vjPNNoahlyhJX1yMi7YH/AANU9UgJxeYvbq45HJjpSQJ1gYEikqaqc0skQt9z+2/7sKqeBE6KyBKgA1BWE4Gba74bmKhOBfpWEdkOXAZ8XzIhljiff36Vx6qhVUBrEWkhIpWBkcC8XGXmAb/1tL53A5JVdV9JB+pDhV6ziDQF5gC/KcPfDrMr9JpVtYWqNlfV5sAHwB/LcBIAd/+2PwZ6ikiIiFQHugKbSzhOX3JzzTtx7oAQkYuANsC2Eo2yZPn886vc3RGoapqIPAh8jvPEwVuqulFERnu2v4HzBMlAYCtwCucbRZnl8prHARcCr3u+IadpGR6wy+U1lyturllVN4vIQuBHIAP4j6rm+xhiWeDy7/w8MF1E1uNUm4xV1TI7KqmIvA9EAnVFZDcQDVQC/31+Wc9iY4yp4Mpj1ZAxxpgisERgjDEVnCUCY4yp4CwRGGNMBWeJwBhjKjhLBMYVERnjeS7dJ+VcHOevItI3n/WRIvLp+R7fy3k7ishAPx17uohs94wM+pOIRGfb9h8RucIf580Vw4OeUStVROpmW1/giJYFjf4pInVE5EsR2eL5WTuf8zX3nOv5bOvqikiqiLzmef8XEXnCf1dtCmOJwLg1BnDzAe+2nFeqOk5Vvzrf4xRDR5xntP3lSVXt6DnP70SkBYCq3quqm/x43kxLgb5AQq71A4DWnuU+YCqAiAQD//RsvwK4LVvCehpYpKqtgUWe9/nZBtyQ7f0twMbzvhLjM5YITA4iUkNEPvOM7b5BREaIyMNAQ2CxiCz2lJsqIqvFGf89xrMuv3LXichyEVkjIrNF5AIR6SIiczzbbxSR0yJSWUSqisg2z/rp4pk/wPON9CcR+Q64KVesb4nIKhH5QUTyjD4qIrHZv+F7jnuz51xvi8h6z75Rnp6rfwVGeL61j3Bzjlznay4im0Xk357fzRciUi2folU9P0969osTkXDP6xMi8qLnb7BCnN6yiMgtnr/JOnGGjigyVf1BVXfks6mgES29jf55I/CO5/U7wNACTnsa2Jx5fcAIYFZx4jf+YYnA5NYf2KuqHVT1SmChqk7BGcskSlWjPOX+5OmZ3B7oLSLtc5fzVD08B/RV1auB1cBjwBrgKs9xegIbgM44wyGszB6MiFQF/g0M9pS9ONvmPwFfq2pnIAqYJCI1cl3PTJwPHjwf9Nfi9Mx8AEBV2wG34XyQBeH0wI5V1Y6qGuvyHLm1Bv6pqm2BJODmbNsmichanPFiZhYwTHQNYIWqdgCWAL/3rB8HXO9ZPyT3TiIS6klg+S2FVTsVNKKlt5EuL8oc2sDzs76X488ERopIYyCdsj22V7ljicDkth7oKyIviUhPVU0uoNytIrIG+AFoi1NtkFs3z/qlng+/3wHNVDUNZ3Cwy3G+cb6MMxlHT+DbXMe4DNiuqls8g4q9l23bdcDTnmPH4XzLbppr/wVAHxGpglO9sURVT+OMvvpfAFX9Caeq5NJ8rsHNOXLbrqprPa/jgebZtmVWDV0MXCsi3fPZ/yyQ2Q6Sff+lOEMp/B5nuIUcVPW4J4HltxRW7VTQiJa+GulyIdAPJ+nGFmN/40flbqwhc35U9RcR6YRTTz5BRL5Q1b9mL+Op134C6KyqiSIynXNVHTmKAl+q6m35bPsW54M5FfgKmI7z4ZZfo2FBHzwC3KyqP3u5njMiEoczVPEI4P1s+7pR6DnykZLtdTrOcNC54zrhiasHsCzX5lQ9N/ZLOp7/p6o6WkS6AoOAtSLSMfsosiISSt5Emun2QpJBQSNaVi5gPcABEWmgqvs81UgFToKjqmdFJB54HOeLw2AvsZgSZncEJgcRaQicUtX3gP/DmTIP4DgQ6nldE6duO9lTfz0g2yGyl1sBXCMirTzHri4imd+6l+A0LC9X1UM4A+JdRt5GxJ+AFiJyied99qTyOfCQiDOKnohcRf5m4gzM1dOzT+b57/DsdynOt/yfc8Vf4DlEpJGILCrgfIUSkRCcqrBfi7DPJaq6UlXHAYfJ+QF9vncEBY1o6W30z3k4d3l4fn7sibOg381knAHhyvoQ6OWOJQKTWzvge09VyJ+AFzzrpwELRGSxqq7DqRLaCLyFU2VBPuUOAXcB74vIjziJ4TJPuZXARTgfyOCMlvljtm/CgPONHucpls88jcXZn3Z5HmdUxh/Fmej7efL3BU7V01eeBk+A14FgcUasjAXuUtUUYDFwRWZjsZdzNKB40yFmthH8iFMNN6eI+673xLEEZ/7eIhGRh8UZ0bIxzjX9x7NpPs7TPVtx2mT+CM7on0Dm6J+bgVmqmpmsJwL9RGQLTrXPRM/6fH83qrpRVd/Jvd4Eno0+akwxiDM08k5VzT02foVnv5uyxxKBMcZUcFY1ZIwxFZwlAmOMqeAsERhjTAVnicAYYyo4SwTGGFPBWSIwxpgKzhKBMcZUcP8Pz29onpXs6s8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "cumVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    cumVote[nBins-b-1] = np.sum(binWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSeats[b] = 1. - cumVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\")\n",
    "ax.set(xlabel=\"statewide vote, nBins =\"+str(nBins)+\",\"+str(STATE), ylabel=\"GOP seats won (no fractl smearing)\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "\n",
    "#LET'S ALSO crudely ESTIMATE RESPONSIVENESS near the STATEWIDE VOTE, with a pseudonormal weighting and lst-sq fit\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=binVote[b]\n",
    "        seatData[counter]=cumSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=binVote[b]\n",
    "            seatData[counter]=cumSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsimple = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "Rx = [stateGOP - 4.*stateSigma, stateGOP + 4.*stateSigma]\n",
    "Ry = [y0 + Rx[0]*Rsimple, y0 + Rx[1]*Rsimple]\n",
    "plt.plot(Rx,Ry ) \n",
    "ax.text(Rx[1]+0.02, Ry[1]+0.02, \"R = \"+str(round(Rsimple,4)), transform=ax.transAxes, fontsize=14,color='purple')\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "ax.text(0.02,expectedSeats+0.03,\"expected seats = \"+str(round(expectedSeats,3)),transform=ax.transAxes,fontsize=9)\n",
    "ax.text(stateGOP+0.01,0.1,\"HD statewide vote = \"+str(round(stateGOP2,3)),transform=ax.transAxes,fontsize=9)\n",
    "Ex = [0,stateGOP ]\n",
    "Ey = [expectedSeats, expectedSeats]\n",
    "plt.plot(Ex,Ey, linestyle='-.',color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "ce47266f-e2ea-4649-b10c-e8225994c122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#3/6/22 - alternate votes-to-seats with fractional seat smearing.\n",
    "# First, compute the general cdf sliver for -3 sigma to +3 sigma\n",
    "# Then, apply this smearing to the binVote weights\n",
    "seatVar = 0.04\n",
    "nBins = 1000\n",
    "nSigma = 6.  #how many total sigma of deviation we are explicitly modeling; the rest go into the tails\n",
    "nSlivers = 2*int(3*nBins*seatVar)  #budget for 3 sigma on either side of the expected vote\n",
    "sliverWt = [0.]*nSlivers  #each sliver holds the cdf reflecting the relative distance from the mean vote\n",
    "sliverSigma = [0.]*nSlivers\n",
    "totalSliverWt = 0.\n",
    "for nS in range(nSlivers):\n",
    "    dSigmaHigh = nSigma* (nS+0.5 - nSlivers/2) / float(nSlivers)\n",
    "    dSigmaLow =  nSigma* (nS-0.5 - nSlivers/2) / float(nSlivers)\n",
    "    sliverSigma[nS] = 0.5*(dSigmaHigh+dSigmaLow)  #for plotting; keeps track of this sliver's center sigma\n",
    "    sliverWt[nS] = norm.cdf(dSigmaHigh) - norm.cdf(dSigmaLow)\n",
    "    totalSliverWt += sliverWt[nS]\n",
    "lowSliverWt = 0.5 * (1. - totalSliverWt)\n",
    "highSliverWt = lowSliverWt\n",
    "#print(\"each tail of distribution has weight\",round(lowSliverWt,4) )\n",
    "#plt.plot(sliverSigma,sliverWt)\n",
    "#plt.show()\n",
    "# OK, that worked as expected.  Now, do the smearing of each bin\n",
    "smearedBinWeight = [0.]*nBins\n",
    "for b in range(nBins):\n",
    "    centerBinNo = b #int(HDvGOP[t]*nBins)   #which vote bin does this tract's vote go into for the expected statewide vote?\n",
    "    binOffset = int(nSlivers/2)\n",
    "    for nS in range(nSlivers):\n",
    "        binNo = centerBinNo + nS - binOffset\n",
    "        if (binNo < 0): #deep blue district; variance would push vote %R < 0\n",
    "            binNo = 0\n",
    "        if (binNo >= nBins):  #deep red district; variance would push vote %R > 1\n",
    "            binNo = nBins - 1\n",
    "        smearedBinWeight[binNo] += binWeight[b]*sliverWt[nS]\n",
    "    # Now put the tails into the correct bins\n",
    "    binNo = centerBinNo -1 - binOffset  #left tail\n",
    "    if binNo < 0:\n",
    "        binNo = 0\n",
    "    smearedBinWeight[binNo] += binWeight[b]*lowSliverWt\n",
    "    binNo = centerBinNo + nSlivers - binOffset  #right tail\n",
    "    if binNo >= nBins:\n",
    "        binNo = nBins -1 \n",
    "    smearedBinWeight[binNo] += binWeight[b]*highSliverWt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(binVote, binWeight, marker='.',linestyle=\"none\",label=\"unsmeared\")\n",
    "plt.plot(binVote, smearedBinWeight, marker='o',linestyle=\"none\",label=\"smeared\")\n",
    "RANGE = [0.0, 0.5*np.max(binWeight)]\n",
    "sGOP = [stateGOP, stateGOP]\n",
    "plt.plot(sGOP,RANGE,linestyle=\"-\",label=\"statewide \"+str(round(stateGOP,3)) )\n",
    "ax.set(xlabel=\"district pct GOP\", ylabel=\"bin weight\")\n",
    "plt.legend()\n",
    "plt.show()   #this should resemble above histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "8a2d7de3-ea93-4447-bb69-36defbae38df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#continuing with votes-to-seats, let's compute the S(V) curve with no fractional-seat smearing\n",
    "smearedBinVote = [0.]*nBins  #this will store the statewide vote for this bin\n",
    "cumSmearedVote = [0.]*nBins\n",
    "for b in range(nBins) :\n",
    "    smearedBinVote[b] = b*dV\n",
    "    cumSmearedVote[nBins-b-1] = np.sum(smearedBinWeight[:(b+1)])  #cumulative weight of all Home Districts below vote b*dV\n",
    "    # print(\"bin, bin vote, cumVote for this bin\",b,b*dV,cumVote[b] )\n",
    "    \n",
    "cumSmearedSeats = [0.]*nBins  #this will sum the seats earned for a given statewide vote\n",
    "stateVoteBin = int( (stateGOP+0.5*dV)*nBins )   #which bin (out of 1/dV) holds the statewide total vote?\n",
    "for b in range(nBins) :\n",
    "    bb = int( max(0,min(nBins-1,nBins/2 + b-stateVoteBin)) )\n",
    "    cumSmearedSeats[b] = 1. - cumSmearedVote[bb]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(smearedBinVote,cumSmearedSeats, marker='o',linestyle=\"none\",label=str(seatVar)+\" smear\")\n",
    "plt.plot(binVote, cumSeats, marker='.',linestyle=\"none\",label=\"no smear\")\n",
    "ax.set(xlabel=\"true \"+STATE+\"-wide vote=\"+str(round(stateGOP,4))+\", nBins =\"+str(nBins), ylabel=\"GOP seats won\")\n",
    "RANGE = [0.1, 0.9]\n",
    "fifty50 = [0.5, 0.5]\n",
    "expected = [stateGOP, stateGOP]\n",
    "expectedSeats = cumSeats[stateVoteBin]\n",
    "expectedS = [expectedSeats,expectedSeats]\n",
    "smearedSeats = cumSmearedSeats[stateVoteBin]\n",
    "smearedS = [smearedSeats,smearedSeats]\n",
    "plt.plot(RANGE,fifty50)\n",
    "plt.plot(fifty50,RANGE)\n",
    "plt.plot(RANGE,smearedS, linestyle=\"--\",color='blue',label=str(round(smearedSeats,3))+\"smeared\")\n",
    "plt.plot(RANGE,expectedS, linestyle=\"--\",color='orange',label=str(round(expectedSeats,3))+\"no smear\")\n",
    "plt.plot(expected,RANGE, linestyle=\"--\",color='red')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "597b7758-4f83-40f7-8909-4946e64267ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "simpler and fractional-seat-smeared (var of 0.04 ) responsiveness are 3.952 3.638\n",
      "fractional expected GOP seats =  7.14  out of  13 seats for MI\n",
      "simpler expected GOP seats =  7.6726  out of  13 seats\n"
     ]
    }
   ],
   "source": [
    "#Finally, let's compare responsiveness using smeared (fractional seat variance) to non-smeared calculated above\n",
    "#LET'S ALSO crudely ESTIMATE (smeared) RESPONSIVENESS near the STATEWIDE VOTE, as we did for non-smeared\n",
    "stateSigma = 0.03  #User-adjustable, this is the uncertainty in the statewide vote from election to election\n",
    "usedBins = int(stateSigma/dV)\n",
    "nFitPoints = 6*usedBins\n",
    "voteData = [0.]*nFitPoints\n",
    "seatData = [0.]*nFitPoints\n",
    "counter = 0\n",
    "for b in range(nBins):\n",
    "    if ( abs(b-stateVoteBin) <= 2*usedBins ):  #include this S-V pair in our line fit\n",
    "        voteData[counter]=smearedBinVote[b]\n",
    "        seatData[counter]=cumSmearedSeats[b]\n",
    "        counter += 1\n",
    "        # print(b,counter)\n",
    "        if ( abs(b-stateVoteBin) < usedBins) : #double count this in data set\n",
    "            voteData[counter]=smearedBinVote[b]\n",
    "            seatData[counter]=cumSmearedSeats[b]\n",
    "            counter +=1\n",
    "fit = np.polyfit(voteData,seatData,1)  #first-order linear regression with old polyfit\n",
    "Rsmeared = fit[0]     #slope is fit[0] in y = mx + b, intercept is fit[1]\n",
    "y0 = fit[1]\n",
    "\n",
    "print(\"simpler and fractional-seat-smeared (var of\",seatVar,\") responsiveness are\",round(Rsimple,3) ,round(Rsmeared,3) )   \n",
    "print(\"fractional expected GOP seats = \",round(nDistricts*smearedSeats,3),\" out of \",nDistricts,\"seats for\",STATE)\n",
    "print(\"simpler expected GOP seats = \",round(nDistricts*expectedSeats,4),\" out of \",nDistricts,\"seats\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f82727-7193-4194-aa61-2b99408dbcb6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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